2026-11-03T12:14:36Z https://ijmrbpdms.org/index.php/files/oai

oai:ojs2.ijerams.org:article/1 2025-08-22T06:25:27Z files:ART

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oai:ojs2.ijerams.org:article/9 2025-08-22T06:25:26Z files:ART

oai:ojs2.ijerams.org:article/11 2025-08-22T06:25:26Z files:ART

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oai:ojs.ijmrbpdms.org:article/14 2026-02-26T07:51:40Z files:ART

CRISPR-Cas9 as a Potential Tool for Precision Medicine: Challenges and Future Directions Dasari Karthik Kumar CRISPR-Cas9, precision medicine, gene editing, therapeutic applications, genetic disorders. The recent discovery of CRISPR-Cas9, a revolutionary gene-editing technology has served as a game-changer in biomedical research since it provides a certain level of precision never before possible in genome editing. In an effort to almost perfectly target specific genetic conditions, CRISPR, as an enabling technology, has a lot of promise in rectifying genetic conditions, refining treatment delivery, and personalizing treatment decisions based on an individuals genetic signature. The present paper examines how CRISPR-Cas9 can be used in precision medicine, outlining its transformative power, limitations and the issues that characterize the use of such a technology in clinical practice. In spite of its effectiveness in preclinical models, safety concerns of off-target effects, ethical issues, and delivery problems continue to present obstacles to the widespread application of its use in clinical settings. The paper contains further detail of progress towards greater specificity and efficacy of CRISPR, along with a detailed overview of where the technique is already used or is anticipated to be used in terms of personalized medicine in the future. The other important considerations including safety, regulatory frameworks, and acceptance that have been raised in the context of clinical translation of CRISPR are also discussed. The proposed research area seeks to illuminate on the ways in which CRISPR-Cas9 may be idealized to be used in a clinic setting as it walks through the intricacies of applying the same to genetic illnesses. The discussion is ended with a glance of future outlook of CRISPR in precision medicine, in regard of whether it could help in treating complex diseases such as cancer, genetic andmultifactorial conditions. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/14 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 1-6 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/14/12 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/15 2026-02-26T07:52:54Z files:ART

Harnessing Plant-based Bioreactors for Sustainable Biopharmaceutical Production Athram Mahesh plant-based bioreactors, biopharmaceutical production, and recombinant proteins, sustainable manufacturing and plant metabolic engineering. The biopharmaceutical industry has had some notable improvements considering the invention of plant based bioreactors, due to the view that it is a cheaper and more environmentally friendly method of therapeutic proteins production. Plant expression systems are most commonly used in technology to produce recombinant protein in plants, as hosts as opposed to mammalian or microbial hosts, have a number of benefits including lowcost, scale-up, and low-risk of human contagion. The paper engages in discourses about the possible use of plant-based bioreactors in manufacturing of biopharmaceutical products with an emphasis on its potential use in sustainable manufacturing. These advantages of plant based systems are discussed in view of the expanding global need to have biopharmaceutical products. The difficulties such as optimization of expression systems and regulatory issues as well as scale-up production are also deliberated. In addition there is also an overview of novel strategies that can be used to increase the efficiency of plant based bioreactors, in that; the techniques include genetic modification of plants and plant metabolic engineering. Using this gap analysis of the status quo of research and development, the paper presents the future potential of plant-based bioreactor in transforming the picture of biopharmaceutical production. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/15 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 7-12 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/15/21 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/16 2026-02-26T07:57:35Z files:ART

Exploring the Role of Microbial Biotechnology in Bioremediation of Heavy Metal Contaminated Environments Deekonda Pranay Kumar microbial biotechnology, bioremediation, heavy metal contamination, biosorption, genetic engineering. Introduction of heavy metals into the environment has become a major challenge globally as they are toxic to the environment, persistent and may be bio concentrated. Conventional processes of heavy metal remediation like, chemical precipitation, adsorption and electrochemical remediation lack considerations to cost-effectiveness and environmental sustainability. A potentially viable alternative appears as microbial biotechnology, which uses the inherent capacities of microorganisms to detoxify and decontaminate heavy metals polluting potentially contaminated sites. The paper discusses areas to be covered concerning the microbial bioremediation effect in the removal of heavy metals, along with the strategies exploited by bacteria, fungi, and algal consortia to lower the amount of deleterious metals in land, enclosed waters, and salt. The most important bioremediation processes including biosorption, bioaccumulation, biomineralization and biotransformation are explained, with an emphasis on their prospects in building and improving the environment cleanup. Issues of scaling microbial bioremediation processes, including variability of microbial activity and contaminated sites complexity, are also discussed. Besides, the combination of synthetic biology and genetic engineering in fine tuning of microbial strains visualizing them in bioremediation is also examined. Lastly, future trends in microbial biotechnology to remediate heavy metals are described in the paper and it is felt that greater focus should now be given to developing sustainable, low cost, and efficient bioremediation solutions to these heavy metals. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/16 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 13-18 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/16/13 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/17 2026-02-26T07:58:57Z files:ART

Advances in Gene Editing for Treating Inherited Genetic Disorders: A Comprehensive Review Edupalli Anusha gene editing, CRISPR-Cas9, inherited genetic disorders, gene therapy, clinical trials. Technologies that deal in gene editing have transformed the medical sector especially in the area of curing inherited genetic cases. The fact that one can now accurately edit the genome of an individual has created opportunities in treating ailments that are a result of a given gene mutation. CRISPR-Cas9 has become the most promising tool among the other methods of gene editing because it is precise, thus easy to process, and flexible enough to be used across a variety of platforms. This review article answers all the research questions comprehensively, giving an overview of the current research achievements in gene editing approaches to treat hereditary genetic diseases and focuses specifically on CRISPR-Cas9 and its functions. We discuss the hidden science behind gene editing, the progress of clinical trials and the recent accomplishments using gene editing as a treatment of inherited illnesses such as sickle cell anemia, cystic fibrosis and Duchenne muscular dystrophy. There is also the issues of gene editing and ethical concerns like off-target effects, the mode of delivery and to what extent germ-line editing may be regulated that are included in the paper. Also, we talk about the future of gene editing technologies and how they may become a canonical method of treatment of inherited genetic illnesses. As depicted in this review, although there is promise and challenges to clinical applications of gene editing, this review provides an eye opener into the future of gene therapies in diseases of inherited genetic predisposition. Keywords: gene editing, CRISPR-Cas9, inherited genetic disorders, gene therapy, clinical trials. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/17 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 19-23 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/17/14 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/18 2026-02-26T08:00:18Z files:ART

Nanotechnology in Dentistry: Enhancing Biomaterial Properties for Tooth Regeneration Pulagara Madhumitha nanotechnology, dental, nanodentistry, dental biomaterials, dental nanomaterials, tooth repair, tooth regeneration, dental nanomaterials. Nanotechnology has brought so much progress in different sectors of medicine and their use in dentistry is an eye opener especially in the developing of biomaterials to growth teeth. The current paper discusses the prospect of nanotechnology in the enhancement of dental materials with emphasis on the use of nanotechnology in regenerating dental structures including dentin, enamel and dentin pulp. Due to this factor, nanoparticles and nanostructured materials have been increasingly used to enhance these mechanical properties of dental materials, their biocompatibility and bioactivity. Such technologies have resulted in new restorative therapeutics and especially in dental fillings, adhesives and coatings. In addition, innovations in nanomaterials development to replicate the natural tooth structure and facilitate teeth tissue regeneration have led to the opening of a new front when it comes to tooth regeneration procedures. The paper gives the detailed analysis of the processes, which lies behind the nanotechnology as a method of improving various properties of dental biomaterials, such as the strength improvement, anti wear, and antibacterial activity. The challenges and limitation of using nanotechnology in the field of dentistry are also discussed including its toxicity, regulatory issues, and problems of stability. Lastly, the future of nanotechnology in tooth regeneration and its development in the clinical practice is discussed. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/18 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 24-30 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/18/15 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/19 2026-02-26T08:02:31Z files:ART

Role of Oral Microbiome in Periodontal Disease and Targeted Therapy Pannati Sai Varun oral microbiome, periodontal pathogens, microbial dysbiosis, precision therapy and probiotics. The mouth microbiome is critical to overall oral health and becomes subject to a number of oral conditions, of which the most prevalent is periodontal disease, when under releasing excess of or lack of balance. Periodontal disease is a long lasting inflammatory process taking place within the tissues supporting the teeth, which often results in the retraction of our gums, movement of our teeth and, eventually, the loss of our teeth should we not receive treatment. It is, in part, it is fuelled by oral microbiome dysbiosis with the overgrowth of pathogenic bacteria and loss of protective species. The topic of interest in this review is the association between the oral microbiome and the development of periodontal disease, including a discussion of microbial changes that are involved in the onset and progression of disease. We also comment on the possibility of targeted treatment (use of probiotics, antimicrobial peptides, and microbiome-based interventions) to restore microbial equilibrium and cure periodontal disease. The current studies have stressed the value of individualized microbiome-based treatment in improving the effectiveness of the treatment and recurrence prevention. Along with promising discoveries, complexities of the oral microbiome, the presence of need of accurate diagnostics, and treatment individualization are issues to be addressed. This review highlights the prospects of microbiome-specific treatment in the management of periodontal diseases with a focus on future clinical development. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/19 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 31-37 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/19/16 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/20 2026-02-26T08:04:23Z files:ART

Impact of Laser Therapy on Pain Management in PostDental Surgery Recovery laser therapy, low level laser therapy (LLLT), dental surgery, pain management, post-operative response. Laser therapy has been established as a prospective adjunct in the management of dental pain, especially during such recovery after a surgical intervention. This paper will hence seek to determine the effect of laser therapy on pain management after dental procedures such as extractions, implant placements, and treatment of soft tissue. Laser treatment especially lowlevel laser treatment (LLLT) has been demonstrated to exhibit the possibility of reducing inflammation, assisting in healing, and pain reduction. The laser therapy, by induction of activity of cells, through stimulation of circulation and through exposure to the modulation of the inflammatory effects, can contribute greatly to recovery after an operation. This review discusses the multiple mechanisms of laser therapy, clinical evidence of its effectiveness in pain management, and the particular advantages of such a procedure in the management of post-operative pain, swelling and discomfort. The paper also focuses on the benefits of the laser therapy in comparison with the conventional pain management approaches such as noninvasive nature, less side-effects, and patient compliance. Also, the demerits and constraints of utilization of laser therapy in the dental clinics including its treatment protocols, cost, and availability are established. The possible future scope of laser treatment in dental surgery as well as on post surgical treatment is also covered. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/20 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 38-43 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/20/17 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/21 2026-02-26T08:05:46Z files:ART

Formulation and Characterization of Biodegradable Drug Delivery Systems for Targeted Cancer Therapy Ramesh Bingi targeted cancer chemotherapy, drug formulation, drug delivery systems, biodegradable, biocompatibility, polymers, drug release. Biodegradable drug delivery system (DDS) is a new technology that gives a great advantage to targeted cancer treatment and refers to remarkable advances in drug potency, patient acceptability and safety. Such systems will be employed to administer directly to cancer cells an agent, which provides a therapeutic effect and over all having lower off-target effects as well as toxicity to healthy cells. This review pays particular attention to the design and development of biodegradable DDS to target cancer therapy with emphasis on choosing materials, drug entrapment methodologies and release rates in designing efficient therapies. The most widely used are the biodegradable polymer types including poly(lactic-co-glycolic acid) (PLGA), polycaprolactone (PCL), and chitosan because of their biocompatibility and the likelihood of their degradation products to be non-toxic. Further, methods of cancer cell targeting involving surface modification, including ligands, antibodies and peptides, are also discussed. The paper addresses strengths and limitations of these systems in cancer treatment such as the effectiveness of these systems to boost therapeutic results and lessen side-effects. Characterization methods have also been discussed in the review in detail comprising particle size analysis, drug loading efficiency, drug release profiles, in vitro and in vivo testing of drug delivery gears. Lastly, the paper points out the prospective areas and possible clinical utilizations of biodegradable DDS on individualized cancer treatment. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/21 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 44-49 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/21/18 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/22 2026-02-26T08:11:29Z files:ART

Pharmacogenomics in Personalized Drug Therapy: Bridging the Gap Between Genetic Variants and Drug Efficacy Banthi lal Bhukya personalized medicine, pharmacogenomics, genetic variance, drug metabolism, drug effect, precision medicine. The field of pharmacogenomics (how genetic differences contribute to differences in reactivity to drugs) is transforming personalized medicine. Pharmacogenomics can play an important role in enhancing drug treatment efficacy and safety by understanding individual pharmacogenetic factors that influence drug metabolism, efficacy and safety and ultimately provide optimized drug therapy. This paper discusses the application of pharmacogenomics in the development of individualized drug therapy, and the ways that genetic differences affect drug reactions, drug response and the response of drug therapy. The paper also addresses the issue of pharmacogenomics incorporation into the clinical practice wherein genetic testing, patient stratification, and the establishment of genotype-guided treatment approaches receive primary attention. The existing issues, including dilemmas regarding the ethics of genetic testing, value-based genetic data, the requirement of healthcare infrastructure to accommodate the development of pharmacogenomes-based drug treatments are also mentioned in the review. In addition, this paper will identify emerging trends in pharmacogenomics, that is, development of genome-wide association studies (GWAS), geneediting tools, and their effect on drug discovery and precision medicine. Finally pharmacogenomics has the potential to close the gap between genetic variation and efficacy of drugs resulting in safer, more effective and personalised drug therapies. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/22 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 50-56 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/22/19 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/23 2026-02-26T08:15:02Z files:ART

Utilizing Artificial Intelligence for Early Diagnosis of Neurodegenerative Diseases: A Machine Learning Approach Pulagara Madhumitha mechanised intelligence, learning, early diagnosis, neuro-degenerative, imagery related to the medical examination, biomarkers. Neurodegenerative diseases, including Alzheimer disease, Parkinson disease and Huntington disease, are challenging the healthcare sector because of their complexities and progressive nature. Diagnosis at an early stage is important to provide an early intervention, yet in the modern approaches to diagnosis, sensitivity and specificity is a primary issue. Artificial intelligence (AI) and machine learning (ML) have seen their recent breakthroughs in recognition and diagnosis of these diseases at an early stage. AI is able to scrutinize the large volumes of medical data to recognize minor patterns and highlight biomarkers that could be overlooked by the other conventional forms of diagnosis. This paper outlines how AI and machine learning can be used to perform an early diagnosis of neurodegenerative diseases, both imaging-based and biomarker-based. It examines different AI algorithms: CNNs (convolutional neural networks), SVM (support vector machine), deep learning models to which medical imaging data (MRI, PET scans) and biomarkers (genomic, proteomic data) are subjected. Also, the paper explains how AI has limitations and pitfalls in neurodegenerative disease diagnosis in terms of data heterogeneity, interpretation, and the requirement of big, high-quality data. The opportunity of AI to transform early diagnosis and make individual approaches to treatment possible is outlined, and how the future of the AI research on neurodegenerative diseases can evolve. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-08-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/23 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 1, August 2025; 57-62 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/23/20 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/24 2026-02-26T09:31:09Z files:ART

The Incidence of Geohelminth Eggs Among Primary SchoolPupils in Kaduna State, North West, Nigeria Danladi Jonah Sarah Nuhu Kase Dennis Amaechi Christy Chinyere Fredrick Garba Ninani Magdalene Joseph Kwaji Mercy Kure Theophilus I. Ojemudia Nyam Agwom Theophilus Geohelminths, Stool samples, Pupils, Unguwan, Kadara The life cycle of Geohelminths requires soil for incubation before becoming infective. The infections of these parasites constitute a major health challenge in sub-Saharan Africa. Methodology: This study was carried out to investigate the incidence of Geohelminths among Primary School Pupils, in Unguwan Kadara, Kaduna State. Three hundred and fifty (350) stool samples were collected. The formal ethyl acetate concentration Technique was used to analyze the Stool samples. Results: Our study revealed the overall prevalence of these Geohelmiths to be 22.6%. Hoewever, for Hookworm infection it was 14.0 %, Ascaris lumbricoides and Taenia spp were 3.1% each and Schistosoma mansoni infection was 2.3 %. The infectivity by age revealed that, (3-5) years had total infectivity of 14.4%, (6-8) years 26.1%, (9-11) years 28.9%, 12-14 years 18.9% and 15 years and above had 50.0% but the difference statistically not significant (p>0.05). Furthermore, infectivity by class distribution showed that primary one,2,3,4,5 and 6 had total infectivity of 15.0%, 23.3%, 23.3%, 33.3%, 20.0% and 20.0% respectively but the statistical difference was not significant (p>0.05). The difference in levels of pupils' knowledge of Geohelminths was statistically significant (p<0.05). Conclusion: Intensive and continuous health education, provision of pipe-borne water, improved environmental sanitation and continuous deworming campaigns in this community will help in reducing the incidence of Geohelminthiasis in Kaduna state. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-11-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/24 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 2, September 2025; 1-7 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/24/22 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/31 2026-02-26T09:06:38Z files:ART

Retinal Organoids in Precision Ophthalmology: Advances, Applications, and Translational Challenges Sameer Ahmad Mir Gojar Achla Sharma Mohita Thakur Pallavi Chandel Retinal organoids, Precision ophthalmology, Gene therapy, Disease modelling, Regenerative medicine, Stem cell technology Retinal organoids have emerged as powerful three-dimensional models that recapitulate human retinal development, cellular diversity, and disease-relevant phenotypes with unprecedented fidelity. Derived from pluripotent stem cells, these organoids provide patientspecific systems for studying inherited and degenerative retinal disorders, enabling mechanistic insights that are often inaccessible in animal models. Advances in bioengineering including microfluidics, bioreactors, RPE co-culture, and 3D bioprinting have significantly improved organoid maturation, photoreceptor functionality, and structural organization. These high-fidelity systems now play a central role in precision ophthalmology, supporting gene therapy validation, CRISPR-based genome editing, drug screening, toxicity profiling, and preclinical transplantation studies. Early clinical interfaces have also begun to emerge, particularly through organoid-derived RPE implantation and the use of patient-specific organoids to guide personalized therapeutic decisions. Despite these advancements, challenges remain, including biological immaturity, lack of vasculature, variability between batches, long culture timelines, and ethical considerations surrounding donor privacy and regulatory oversight. Continued efforts toward standardization, integration of aging features, development of organoid biobanks, and adoption of AI-driven analysis will accelerate the safe and effective translation of retinal organoid technologies into clinical therapies. Collectively, retinal organoids represent a transformative platform poised to reshape the future of personalized vision care. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-11-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/31 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 2, September 2025; 8-25 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/31/23 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/32 2026-02-26T09:08:34Z files:ART

Conquering the Solid Frontier: Next-Generation CAR-T Cell Immunotherapy Beyond Hematologic Malignancies - Transformative Strategies, Emerging Targets, and Future Paradigms Mohammad Anees Rather Tawqeer Shafi Pallavi Chandel Sumairah Qadir Checkpoint inhibitors, Combination therapy, Biomarker development, Global healthcare impact, CAR-T cell therapy, Synthetic biology. Chimeric antigen receptor T-cell therapy has revolutionized the management of blood cancers yet its application in solid tumors has been restricted because of the complicated tumor microenvironment, differences in tumor antigens and effective immune-evasion strategies. Recent developments are concerned with advanced engineering practices in order to overcome these problems. Cytokine releasing (interleukin-12 or interleukin-18) armored chimeric antigen receptor T cells activate immune responses in suppressive tumor microclimates. Designs of logic-gated chimeric antigen receptor which need two antigen recognition improve tumor specificity and minimize harm to normal tissues. Metabolic and epigenetic reprogramming strategies are being created to enhance the survival of chimeric antigen receptor T-cells, retain stem-like features, and be able to function in low-oxygen and nutrient-deprived environments. Mesothelin, glypican-3, human epidermal growth factor receptor-2, mucin-1, and B7-H3 are some of the new tumor-associated targets that are broadening therapeutic opportunities in various solid tumors. The tools of synthetic biology, such as inducible promoters and self-regulating feedback controls, can enable the accurate regulation of the activation of chimeric antigen receptor T- cells and enhance safety. Also, artificial intelligence, personalized antigen profiling, and optimization based on biomarkers are integrated to develop highly customized chimeric antigen receptor T-cell products. The combination of these innovations is a significant change to more potent, multi-purpose, and smartly designed T-cell therapies that can destroy the obstacles of solid tumor. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-11-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/32 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 2, September 2025; 26-48 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/32/24 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/34 2026-02-26T09:10:05Z files:ART

Role of Pharmacogenomics in Personalized Medicine: A New Frontier in Drug Therapy Optimization Daiyan Mojeeb Mohita Thakur Rasikh Shafi Khan Pallavi Chandel Achla Sharma Pharmacogenomics, Drug Therapy Optimization, Warfarin, Clopidogrel, Pharmacogenomic Testing, Clinical Pharmacists. Pharmacogenomics integrates pharmacology and genomics to study how genetic variations influence individual responses to medications. Incorporating genetic data into clinical decision-making allows more precise drug selection and dosing, improving effectiveness, reducing adverse effects, and promoting truly individualized therapy. This review outlines the development and clinical importance of pharmacogenomics and highlights its application in drugs such as warfarin and clopidogrel, where gene variants including CYP2C9, VKORC1, and CYP2C19 significantly affect metabolism and treatment outcomes. Genetic testing helps determine appropriate dosing to achieve optimal anticoagulant and antiplatelet effects. The review also emphasizes pharmacogenomics as a foundation of precision medicine, enabling clinicians to understand how genes influence drug metabolism, transport, and targets. Clinical pharmacists play a key role through patient counselling, dose adjustments, and integrating genotype-guided therapy into practice. Despite its promise, challenges such as high testing costs, limited awareness, and concerns about privacy and consent continue to hinder widespread adoption. As genomic technologies, bioinformatics, and digital tools advance, pharmacogenomic data are becoming easier to apply in routine care. Ultimately, pharmacogenomics is set to reshape modern therapeutics by shifting drug therapy toward a more predictive, preventive, and patient-centred approach. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-11-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/34 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 2, September 2025; 49-61 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/34/25 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/35 2026-02-26T09:11:20Z files:ART

Targeted Nanomedicine in Oncology: Novel Mechanisms of Abraxane in Overcoming Resistance in Breast Cancer Ashok Kumar Pallavi Chande Achla Sharma Abraxane, nanomedicine, breast cancer, immunotherapy, nanoparticle drug delivery, tumour microenvironment. Breast cancer remains a major global health problem, and treatment resistance is a major barrier to successful therapy. Conventional paclitaxel has limitations such as poor solubility, solvent-related toxicity, and drug efflux–mediated resistance. Nanomedicine offers solutions to these problems, and Abraxane is one such clinically proven nano formulation that improves drug delivery and therapeutic outcomes. This review examines how Abraxane helps overcome paclitaxel resistance, summarizes key preclinical and clinical findings, and discusses future directions for nanotechnology-based treatments. A systematic search was conducted using PubMed, Google Scholar, and clinical trial databases to evaluate mechanisms related to nanocarrier function, tumour microenvironment changes, efflux avoidance, and clinical performance. Abraxane shows higher tumour uptake through gp60-mediated transcytosis, SPARC-based stromal binding, and enhanced vascular permeability. These mechanisms help the drug bypass P-glycoprotein efflux, increase intracellular paclitaxel levels, and favourably modify the tumour microenvironment. Clinical studies, including CALGB 40502 and IMpassion130, report better responses, improved progressionfree survival, and reduced toxicity compared with solvent-based paclitaxel. Additionally, Abraxane enhances immune activity and improves response to checkpoint inhibitors. Abraxane demonstrates how nanomedicine can overcome drug resistance and improve outcomes in breast cancer. Continued development of multifunctional and biomarkerguided nanocarriers may further support personalized cancer therapy in the future. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-11-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/35 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 2, September 2025; 62-78 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/35/26 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/37 2026-02-26T09:32:31Z files:ART

Smart Nanotechnology-Based Drug Delivery Systems: Recent Advances and Future Prospects in Stimuli-Responsive Targeted Therapy Firdosa Akhter Pallavi Chandel Achla Sharma Mohita Thakur Shahnawaz Alam Zahid Ali Smart Nanocarriers, Stimuli-Responsive Drug Delivery, Targeted Therapy, Controlled Drug Release, Nanomedicines, Personalized Medicine. Drug Delivery System based on smart nanotechnology allow stimuli-responsive and spatiotemporally regulated therapeutic release, which is a revolutionary development in precision medicine. When compared to traditional drug formulations, engineered nanocarriers, such as liposomes, polymeric nanoparticles, dendrimers, micelles, solid lipid nanoparticle NPs, and hybrid inorganic platforms, display improved bioavailability, reduced off-target toxicity, and superior pharmacokinetic performance. Therapeutic payloads can be released site-specifically and on demand thanks to these nano systems ability to react to endogenous cues (pH, redox gradients, enzymatic activity, and hypoxia) or exogenous stimuli (temperature, light, ultrasonic, magnetic, and electric fields). Their selectivity and biological performance are further enhanced by the incorporation of targeted ligands, surface modifications, and biocompatible materials across intricate pathophysiological barriers, such as the blood–brain barrier and the tumour microenvironment. In cancer, neurodegenerative, cardiovascular, and infectious illnesses, smart nanocarriers have shown encouraging efficacy. They have also opened the door for sophisticated theragnostic applications that combine therapy and diagnostics. Translational issues such as large-scale repeatability, long-term stability, immunogenicity, and regulatory uniformity still exist despite tremendous advancements. These obstacles should be addressed by the confluence of artificial intelligence, 3D/4D printing, and nanorobotics, which will hasten the clinical translation of next-generation intelligent nanomedicines toward tailored and flexible therapy paradigms. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/37 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 3, October 2025; 1-18 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/37/28 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/38 2026-02-26T09:33:35Z files:ART

Natural Products and Phytochemicals in Modern Drug Discovery: Mechanistic Insights, Barriers, and Future Opportunities Zahid Ali Pallavi Chandel Achla Sharma Mohita Thakur Firdosa Akhter Natural products, Phytochemicals, Drug discovery, Bioavailability, Nanotechnology, Synthetic biology. Natural products have long been central to therapeutic development, offering structurally diverse compounds that continue to inspire modern drug discovery. Phytochemicals including polyphenols, flavonoids, terpenoids, and alkaloids exert multi-target actions that regulate oxidative stress, inflammation, apoptosis, and immune and metabolic pathways. These pleiotropic effects make them promising candidates for treating complex, multifactorial diseases such as cancer, cardiovascular disorders, diabetes, and neurodegenerative conditions. Despite their therapeutic potential, the clinical progression of natural products is restricted by poor solubility, low bioavailability, metabolic instability, and variability arising from environmental and seasonal factors. Limited standardization and insufficient large-scale clinical trials further impede regulatory approval.Recent technological advances are helping overcome these limitations. Modern extraction techniques, advanced chromatographic and spectroscopic profiling, structural modification strategies, nanocarrier-based delivery systems, and synthetic biology platforms have improved the stability, yield, and pharmacokinetic performance of natural compounds. Omics-driven analyses and computational modelling have enhanced understanding of molecular mechanisms and facilitated more efficient identification and optimization of bioactive leads.By integrating traditional pharmacognosy with emerging scientific innovations, natural products and phytochemicals remain vital contributors to future drug discovery, offering sustainable, mechanistically rich, and clinically relevant avenues for therapeutic development. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/38 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 3, October 2025; 19-33 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/38/29 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/39 2026-02-26T09:34:48Z files:ART

Evaluating the Efficacy of CRISPR-Based Point-of-Care Diagnostics for Rapid Detection of Multi-Drug Resistant Tuberculosis Shameer shaik CRISPR diagnostics, MDR-TB, point-of-care testing, Cas12a, rapid testing. Multi-drug resistant tuberculosis (MDR-TB) is one of the most significant health issues in the world as it is diagnosed late, there are failures in treatment, and access to local and quick diagnostic methods is insufficient. Traditional techniques such as culture-based and molecular assays are either time consuming or demand special laboratory facilities. The recent advances in CRISPR-based diagnostics have allowed detecting genetic markers related to drug-resistant Mycobacterium tuberculosis quickly, sensitively, and at the point of care. This paper assesses the effectiveness of CRISPR-Cas12a and Cas13a-based detection platforms in detecting MDRTB in terms of sensitivity, specificity, turnaround time, and their applicability in decentralized clinical environments. On a cross-sectional design, samples of suspected MDR-TB patients were tested on sputum through CRISPR-based assays and compared to those tested on GeneXpert and standard culture methods. Findings indicated that CRISPR tests were highly accurate in finding mutations linked to resistance in rpoB, katG and inhA promoter areas. The turnaround time was lessened to less than an hour and the assays worked efficiently without the use of advanced apparatus. The results indicate that CRISPR-based point-of-care diagnostics has a major potential to help in enhancing early patient detection, transmission reduction, and prompt therapeutic decisions in high-burden areas. Nonetheless, full-scale validation, field implementation research and cost-effectiveness analysis are necessary in order to be integrated into national TB control initiatives. The present study underscores CRISPR diagnostics as a novel accolade to the international MDR-TB diagnostic approaches. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/39 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 3, October 2025; 34-38 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/39/30 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/40 2026-02-26T09:36:02Z files:ART

A Prospective Study on Post-Viral Cardiac Remodeling in Young Adults Recovering from Dengue Infection Shanmukhi Bejagam recovery after dengue, cardiac remodelling, myocardial failure, young adults, prospective study. Dengue infection is a virus that is spreading very fast among the world in terms of the number of cases, and although the acute symptoms of the condition were well characterized, the chronic cardiac effects are barely known. Recent studies indicate that dengue has the possibility of causing subclinical myocardial inflammation, autonomic imbalance, and structural remodelling which may extend beyond the acute. This is a prospective study that attempts to assess patterns of cardiac remodeling in young adults (1830 years old) after laboratory-confirmed dengue infection. One hundred and twenty individuals were continued during 12 weeks after recovery and administered to serial measurements comprising echocardiography, electrocardiography, cardiac biomarkers (troponin-I, NT-proBNP), and heart rate variability. The findings showed that 28% of the participants had temporary left ventricular (LV) diastolic dysfunction and 16% of the subjects presented with continuing low global longitudinal strain measures. Echocardiographic abnormalities were associated with the increased levels of biomarkers, indicating that myocardial stress is still present. Even though the majority of changes were resolved by week 12, some of them revealed delicate but consistent deviations of structures and functions, which are typical of early remodeling. These results indicate that dengue should be considered as a possible cause of post-viral cardiac sequelae, particularly in younger populations that used to be regarded as low-risk of cardiovascular complications. Follow up imaging, early screening and risk-stratified management could be used to prevent the development of long-term dysfunction. The research suggests that further research should be conducted using larger multi-centered longitudinal studies to confirm these early results and identify the mechanisms. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/40 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 3, October 2025; 39-44 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/40/31 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/41 2026-02-26T09:37:56Z files:ART

Characterizing Early Neurovascular Changes in Mild Cognitive Impairment Using Advanced Functional MRI Mapping Shaik Shukoor mild impaired cognitive, neurovascular coupling, fMRI, cerebral blood flow, default mode network. Mild Cognitive Impairment (MCI) is an intermediate period between aging and the early phases of Alzheimer disease and often contains subtle neurovascular malfunction, which is eventually associated with observable structural atrophy. Functional MRI (fMRI) especially arterial spin labeling (ASL) and blood oxygen level dependent (BOLD) imaging provides noninvasive methods to determine early neurovascular changes. This is a prospective study that examines neurovascular coupling, regional cerebral blood flow (rCBF), and changes in hemodynamic responses to the individuals with MCI through advanced fMRI mapping. Another group of 110 (60 MCI and 50 control participants) aged 55-72 years received ASL, resting-state fMRI and task-based BOLD mapping during 6 months. Findings showed that there were great decreases in rCBF in the hippocampus, posterior cingulate cortex, and precuneus of the MCI group. There were delayed and reduced functional connectivity in default mode network (DMN) nodes. Pattern recognition of fMRI signals by use of machine-learning showed that 85 percent of the samples (MCI and controls) could be classified correctly. Importantly, neuropsychological scores, namely memory and executive function scores, had a significant correlation with neurovascular changes. The paper provides insights into the importance of the state-of-the-art fMRI methodologies in the early characterization of the neurovascular dysfunction in MCI with reference to the possible application in early diagnosis, risk identification, and therapeutic assessment. These findings need to be validated by longitudinal and large-scale studies based on the integration of multimodal biomarkers to determine clinical protocols. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/41 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 3, October 2025; 45-50 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/41/32 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/42 2026-02-26T09:40:51Z files:ART

Edge-AI Enabled Intrusion Detection Framework for Ultra-LowLatency IoT Networks in Smart Cities Shaik Shukoor Edge AI, intrusion detection, IoT security, smart cities, ultra-low latency. Internet of Things (IoT) infrastructures are highly dependent on smart cities to be able to handle basic amenities like intelligent transportation and smart utilities, community security, and environmental management. Nevertheless, the spread of IoT devices with limited resources opens networks to cyberattacks that may interfere with important services. Conventional cloud-based intrusion detection systems (IDS) add spoilage and bandwidth and limited scalability. Recent developments in edge computing and minimalistic artificial intelligence models promise the solutions to real-time threat mitigation. This paper will propose a prototype of an Edge-AI based intrusion detection system, which can be used in ultra-low-latency IoT networks used in smart city architectures. A simulated and real-world dataset of IoT traffic was trained on lightweight machinelearning models, such as MobileNet-V3 embedded classifiers, optimized Random Forest, and quantized neural networks to be deployed on the edge. The paper tested the performance of the model-based edge devices, including the NVIDIA Jetson nano, Google Coral TPU, and ARM-based microcontrollers. The proposed structure proved to have an average detection rate of 94.2 with latency of less than 12ms and a 38 percent energy saving over cloud based IDS. The experimental findings forwarded that the implementation of AI on the network edge can significantly accelerate the time of threat response, reduce the loss of packets, and increase the general system resilience. The results reveal that Edge-AI models can be successfully applied to achieve ultra-lowlatency IoT networks, which can be scaled and autonomously used in the cybersecurity of smart cities. It is suggested that the large-scale deployments and federated learning should be integrated in the future to increase robustness or privacy. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/42 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 4, November 2025; 1-6 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/42/33 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/43 2026-07-11T10:05:50Z files:ART

Determination of Glucose levels and Total Antioxidant Status of Patients with Gestational Diabetes Mellitus in Kaduna State Nigeria. Sarah Nuhu Kase Friday Iweka Dennis Amaechi Christy Chinyere Fredrick Garba Ninani Fatima Lami Ciroma Nasiru Lawal Mercy Kure Jamila Ibrahim Suleiman Danladi Jonah gestational diabetes, antioxidants, pregnancy,vitamin C, glucose Gestational Diabetes Mellitus (GDM) is a form of pregnancy-induced hyperglycemia associated with insulin resistance. It is commonly diagnosed during the second or third trimester and poses significant risks to both the mother and fetus if not adequately managed. This study aimed to evaluate the impact of GDM on total antioxidant status (TAS) among affected individuals.A total of 160 consenting women aged 18–40 years at 24–28 weeks of gestation were recruited. Sixty participants were diagnosed with GDM, while 50 non-diabetic pregnant women served as Control 1. All were attending antenatal clinics at Barau Dikko Teaching Hospital, Yusuf Dantsoho Memorial Hospital, and Gwamna Awan General Hospital. An additional 50 apparently healthy non-diabetic, non-pregnant women (Control 2) were selected from staff of these facilities. Screening was conducted using a 50 g oral glucose challenge test (OGCT), followed by a fasting 75 g oral glucose tolerance test (OGTT). Approximately 5 mL of blood was collected and analyzed using standard laboratory procedures. Data were analyzed using appropriate statistical methods, with significance set at p < 0.05. The mean TAS values in OGCT (random blood glucose) and OGTT (fasting blood glucose) samples were significantly lower in GDM and Control 1 groups (5.04 ± 0.19, 5.64 ± 0.14 and 5.62 ± 0.21, 5.39 ± 0.10 µmol/L, respectively) compared to Control 2 (7.14 ± 0.24 µmol/L). Serum glucose levels were significantly elevated in GDM subjects relative to controls. Additionally, significant relationships were observed among adiponectin, TAS, superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase, malondialdehyde (MDA), and vitamin C levels across study groups.These findings highlight the role of oxidative stress in GDM and may provide valuable insights for improving clinical management and therapeutic strategies for affected patients. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-01-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/43 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 1, January 2026; 1-14 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/43/34 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/44 2026-07-11T10:05:50Z files:ART

Evaluation of serum levels of oxidative stress markers and Cardiovascular Risk Among Adult HIV Patients on Dolutegravirbased Therapy in Benue State, Nigeria. S.A Agada D.E Uti C.O Ogbu V. Abah A.B Onoja.M.C Adilieje C.O Ezeh J.E Ikekpeazu HIV, oxidative stress, dolutegravir, TLD, GPx, SOD, catalase, MDA, antiretroviral therapy, Nigeria Background: Human Immunodeficiency Virus (HIV) remains a major global health burden, with approximately 40.8 million people affected worldwide and about 1.9 million in Nigeria as of 2024. Although dolutegravir-based antiretroviral therapy (TLD) has improved patient outcomes, concerns persist regarding its association with oxidative stress and cardiovascular risk. Methods: A cross-sectional study was conducted among 400 adults (≥18 years) recruited from three HIV treatment centers in Benue State, Nigeria. Participants were grouped into five categories (n = 80 each): HIV-negative controls, newly diagnosed HIV-positive individuals not yet on TLD, HIV-positive individuals on TLD for <1 year, those on TLD for >1 year, and individuals switched from TLE to TLD. Serum levels of glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA) were measured using colorimetric assays. Results: Antioxidant enzyme activities (GPx, SOD, CAT) decreased significantly across the groups (p < 0.0001), while MDA levels increased significantly (p < 0.0001), indicating elevated oxidative stress.Conclusion: TLD-based therapy is associated with increased oxidative stress among HIV patients in Benue State. Routinemonitoring of oxidative stress markers as markers of cardiovasculardiseases is recommended to improve long-term clinical outcomes. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-05-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/44 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 1, January 2026; 15-27 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/44/35 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/45 2026-07-11T10:05:50Z files:ART

Biodegradation Potential of Indigenous Fungal Isolates on Potato (Solanum tuberosum L.) Waste: A Case Study of Dumpsites in Bwari, Abuja. Anslem S. Maichiki Anthonia Oyegue O Izevbigie R. Adesuwa Biodegradation; Potato waste; Aspergillus niger; Aspergillus flavus; Penicillium sp.; Dumpsite; Mycoremediation; Sustainable waste management. Introduction: Wastes are unwanted by-products from daily activities like consumption and production, which are no longer useful. Improper disposal, especially open dumping, poses immediate health risks and long-term environmental threats, including greenhouse gas emissions. The accumulation of organic waste like potato peels in dumpsites leads to foul odors and the spread of pathogens . This study aimed to screen the biodegradation potential of fungal species isolated from dumpsites at Bwari, Abuja, Nigeria, for the management of potato waste. Methods A total of six soil samples were collected from three major dumpsites within the Bwari Area Council, FCT, Abuja. Standard microbiological techniques, including serial dilution and plating on Potato Dextrose Agar (PDA), were used to isolate and enumerate fungi. Isolates were characterized and identified based on macroscopic and microscopic morphological features. The biodegradation potential of the identified fungal isolates was assessed using a 7-day weight loss experiment in a minimal salt broth medium, and the percentage weight loss was calculated. Results The physicochemical analysis of the soil revealed a temperature range of 28–31°C and pH values ranging from 7.19 to 9.12. The heterotrophic fungal count ranged from 5 × 10⁻⁴ to 12 × 10⁻⁴ CFU/g. Morphological characterization led to the identification of three dominant genera: Aspergillus flavus (40.00% prevalence), Aspergillus niger (33.33%), and Penicillium sp. (26.67%). The single isolate with the highest degradation efficiency was A. niger (82.0% weight loss), followed by A. flavus (75.0%) and Penicillium sp. (70.0%). Notably, the consortium of A. flavus and A. niger achieved the highest overall degradation of 90.0%, demonstrating a synergistic effect. A drop in pH (from alkaline to near-neutral/acidic) was observed in all treatment setups, indicating microbial metabolic activity. Conclusion This study demonstrates that indigenous fungal species, particularly Aspergillus niger and its consortium with Aspergillus flavus, possess significant potential for the rapid biodegradation of potato waste. These findings support the feasibility of utilizing native fungal strains as low-cost, eco-friendly agents for organic waste management, contributing to pollution reduction in urban and academic settings. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-01-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/45 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 1, January 2026; 28-34 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/45/37 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/46 2026-07-11T10:05:50Z files:ART

Antibacterial Activity of Curcuma longa (Turmeric) Ethanolic Extract Against Staphylococcus aureus and E. coli: An In Vitro Study Oyegue, Anthonia O Anslem S. Maichiki Olowosoyo, Ebunoluwa Grace Curcuma longa; turmeric; antibacterial activity; Staphylococcus aureus; E. coli; agar well diffusion; antimicrobial resistance. Background: Antimicrobial resistance (AMR) is a growing global public health crisis that undermines the effectiveness of conventional antibiotics. Natural plant-derived compounds have received renewed attention as alternative or adjunct antimicrobial agents. Curcuma longa (turmeric), a member of the Zingiberaceae family, contains bioactive constituents, notably curcumin, flavonoids, tannins, and alkaloids, with well-documented antibacterial properties. The aim of this study is to evaluate the in vitro antibacterial activity of the ethanolic extract of Curcuma longa rhizomes against Staphylococcus aureus and E. coli. Methods: Fresh rhizomes of C. longa were sourced from Dei Dei Market, Abuja, Nigeria, and extracted by cold maceration in 90% ethanol for 72 hours. Staphylococcus aureus was isolated from a throat swab and confirmed by Mannitol Salt Agar (MSA) morphology, Gram staining, and catalase and coagulase tests. E. coli was isolated from a campus tap water sample and confirmed on Eosin Methylene Blue (EMB) Agar with positive indole and methyl red tests. Antibacterial activity was assessed by agar well diffusion on Mueller-Hinton Agar (MHA) at six extract concentrations (50, 100, 200, 400, 800, and 1000 mg/mL). Ciprofloxacin (5 µg) and Ampicillin (10 µg) served as positive controls; DMSO served as the negative control. Results: No inhibitory activity was observed at 50–200 mg/mL for either organism. At 400, 800, and 1000 mg/mL, mean zones of inhibition (ZOI) against S. aureus were 15.0, 22.0, and 27.5 mm respectively; against E. coli, ZOI were 11.5, 17.0, and 21.5 mm. Staphylococcus aureus was consistently more susceptible than E. coli at all active concentrations. At 1000 mg/mL, the extract marginally exceeded both the Ciprofloxacin (26.0 mm) and Ampicillin (21.0 mm) positive controls. Conclusion: Locally sourced Nigerian turmeric demonstrates significant, concentration-dependent antibacterial activity against both test organisms, with S. aureus being more susceptible than E. coli, consistent with the structural differences between Gram-positive and Gram-negative cell walls. These findings support C. longa as a promising natural antimicrobial candidate in the context of antimicrobial resistance. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-01-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/46 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 1, January 2026; 35-42 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/46/38 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/47 2026-07-11T10:05:50Z files:ART

Explainable Artificial Intelligence for Predicting Cancer Treatment Toxicity in Real-World Clinical Practice Dr. Jayanthi Kanaka Ram Artificial intelligence; machine learning; deep learning; cancer treatment toxicity; real-world data; electronic health records; precision oncology; foundation models; large language models. Cancer treatment–related toxicities remain a major challenge in oncology, frequently compromising patient quality of life, treatment adherence, therapeutic efficacy, and overall survival. The increasing availability of real-world clinical data (RWD), combined with advances in artificial intelligence (AI) and machine learning (ML), has created new opportunities for the early prediction of treatment-related adverse events and personalized toxicity risk assessment. This review provides a comprehensive overview of AI-driven approaches for predicting cancer treatment toxicity using real-world clinical data. It examines the application of conventional machine learning, deep learning, and emerging foundation models across diverse data sources, including electronic health records, genomic and molecular datasets, medical imaging, laboratory parameters, wearable device data, and patient-reported outcomes. Machine learning models have demonstrated encouraging predictive performance for identifying treatment-related adverse events. Among the commonly used algorithms, random forest has been the most frequently applied, followed by support vector machines, XGBoost, decision trees, and LightGBM. A recent meta-analysis reported a pooled sensitivity of 0.65, specificity of 0.89, and an area under the receiver operating characteristic curve (AUC) of 0.8069, highlighting the potential of AI-based models for clinical toxicity prediction. Recent advances in transformer-based architectures, multimodal learning, and large language models have further improved predictive accuracy and model generalizability across multiple cancer types, treatment modalities, and healthcare settings. Foundation models trained on large-scale multimodal real-world datasets are emerging as promising clinical decision-support tools capable of integrating heterogeneous patient information for individualized toxicity prediction. AI-driven toxicity prediction has the potential to transform precision oncology by enabling proactive patient monitoring, individualized treatment planning, dose optimization, early intervention, reduced treatment-related hospitalizations, and improved clinical outcomes. Despite these advances, challenges related to data quality, model interpretability, external validation, fairness, and seamless clinical implementation remain significant barriers to widespread adoption. This review summarizes current evidence, discusses emerging technologies, identifies existing limitations, and highlights future directions for integrating AI-driven toxicity prediction into routine oncology practice. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-01-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/47 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 1, January 2026; 43-54 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/47/39 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/48 2026-06-30T11:26:42Z files:ART

Artificial Intelligence–Driven Drug Repurposing and Novel Target Discovery in Precision Oncology Dr. Jayanthi Kanaka Ram Mr. Arvind K. Nair Artificial intelligence, machine learning, deep learning, drug repurposing, target discovery, precision oncology, drug–target interaction, graph neural networks, foundation models, generative AI, multi-omics integration, biomarker discovery. Cancer continues to impose a substantial global health burden, while conventional oncology drug discovery remains constrained by lengthy development timelines, escalating costs, and high rates of clinical failure. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is transforming this landscape by enabling rapid identification of therapeutic candidates and novel molecular targets. This narrative review summarizes recent advances (2020–2026) in AI-driven drug repurposing and target discovery for cancer, highlighting computational approaches that integrate multi-omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and real-world clinical information. We discuss state-of-the-art methodologies such as supervised and unsupervised learning, graph neural networks, deep neural networks, transformer-based architectures, foundation models, and generative AI for predicting drug–target interactions, prioritizing biomarkers, and identifying repurposing opportunities for approved therapeutics. Across diverse malignancies, these approaches have demonstrated improved predictive performance, accelerated therapeutic discovery, and enhanced precision medicine strategies by facilitating personalized treatment selection and biomarker-guided interventions. Despite these advances, important challenges remain, including heterogeneous data quality, limited model interpretability, external validation, regulatory considerations, and integration into routine clinical workflows. Emerging developments in explainable AI, federated learning, multimodal foundation models, digital twins, and prospective clinical validation are expected to further strengthen the reliability and clinical applicability of AI-driven oncology drug discovery. Collectively, AI has the potential to fundamentally reshape precision oncology by accelerating the discovery of safe, effective, and personalized cancer therapies while reducing development costs and time to clinical translation. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-03-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/48 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 3, March 2026; 01-10 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/48/40 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/49 2026-06-30T11:27:38Z files:ART

Advancing Multi-Center Cancer Research Through Federated Learning: Privacy-Preserving Artificial Intelligence in Oncology Dr. Jayanthi Kanaka Ram Federated learning; Artificial intelligence; Oncology; Privacy-preserving AI; Precision oncology; Cancer imaging; Multi-center research; Machine learning Cancer remains one of the leading causes of morbidity and mortality worldwide, underscoring the urgent need for advanced computational approaches that can improve cancer diagnosis, prognosis, and personalized treatment. Artificial intelligence (AI) has emerged as a transformative technology in oncology, enabling automated medical image interpretation, biomarker discovery, treatment response prediction, and integration of multimodal biomedical data. However, most conventional AI models rely on centralized data aggregation, requiring patient information from multiple institutions to be transferred to a central repository for model development. Such approaches raise significant concerns regarding patient privacy, data ownership, regulatory compliance, cybersecurity, and institutional data-sharing restrictions, thereby limiting large-scale collaborative cancer research. Federated learning (FL) has emerged as a decentralized, privacy-preserving AI paradigm that enables multiple healthcare institutions to collaboratively train machine learning models without exchanging raw patient data. By keeping sensitive data within local institutions and sharing only encrypted model parameters or updates, FL facilitates secure collaboration while maintaining data confidentiality. In oncology, FL has demonstrated considerable potential for distributed analysis of medical imaging, genomic and multi-omics data, electronic health records, digital pathology, and clinical trial datasets, thereby supporting improved cancer detection, tumor characterization, disease progression modeling, biomarker discovery, and personalized therapeutic decision-making. Despite these advantages, widespread implementation of FL in oncology remains challenged by data heterogeneity, non-identically distributed datasets, communication overhead, computational resource variability, model bias, cybersecurity threats, and the absence of standardized validation and regulatory frameworks. Future integration of FL with deep learning, foundation models, generative artificial intelligence, multimodal learning, multi-omics analytics, and international cancer research networks is expected to accelerate the development of secure, collaborative, and privacy-preserving precision oncology platforms. This review provides a comprehensive overview of federated learning principles, system architectures, security mechanisms, clinical applications, current challenges, and future research directions, highlighting its growing role in enabling privacy-preserving artificial intelligence for multi-center cancer research.oncology practice. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-04-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/49 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 4, April 2026; 01-09 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/49/41 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/50 2026-07-01T05:14:43Z files:ART

Integrative Multi-Omics Machine Learning for Precision Cancer Prognostication Dr. Vikram J. Patel Dr. Ananya T. George Multi-omics, machine learning, deep learning, cancer prognosis, precision oncology, data integration, survival prediction Background: Cancer remains a leading cause of mortality worldwide, necessitating accurate prognostic tools to guide clinical decision-making and personalized treatment strategies. Traditional prognostic models based on clinicopathological features demonstrate limited predictive accuracy due to the inherent molecular heterogeneity of malignancies.This narrative review examines recent advances in multi-omics machine learning approaches for cancer prognosis prediction, synthesizing evidence from 2020–2026 on integration strategies, algorithmic methodologies, and clinical applications across major cancer types.Deep learning enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. Multi-omics integration incorporating genomics, transcriptomics, epigenomics, proteomics, and metabolomics consistently outperforms single-omics approaches. DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches, robustly predicts patient survival subtypes using multi-omics data and yields significantly better risk-stratification than other multi-omics integration methods. Graph neural networks, transformer-based architectures, and attention mechanisms have emerged as powerful tools for capturing complex inter-omics relationships. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. These models demonstrate substantial improvements in survival prediction, recurrence risk stratification, and treatment response assessment across breast, lung, colorectal, liver, and hematological malignancies. Meta-learning, spatial multi-omics, and federated learning are pivotal directions for realizing the clinical translation of next-generation precision oncology. Addressing challenges in data harmonization, model interpretability, and prospective validation remains essential for clinical implementation. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-02-27 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/50 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 2, February 2026; 01-10 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/50/42 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/51 2026-07-10T06:30:37Z files:ART

Digital Twins for Personalized Management of Gynecologic Malignancies Mr. Ajay Kuldeep Dr. Divya Menon Mrs. Bhavana Rao Dr. Rahul Srivastava Dr. Komal Desai Digital twins, Gynecologic oncology, Precision medicine, Artificial intelligence, Ovarian cancer, Cervical cancer, Endometrial cancer, Multimodal learning, Personalized treatment, Computational oncology. Background: Gynecologic malignancies, including cancers of the ovary, cervix, endometrium, vulva, and vagina, remain a major cause of cancerrelated morbidity and mortality among women worldwide despite significant advances in screening, molecular diagnostics, targeted therapies, immunotherapy, and minimally invasive surgical techniques. The biological heterogeneity of these malignancies, coupled with diverse genomic alterations, tumor microenvironment dynamics, hormonal influences, and patient-specific clinical characteristics, presents considerable challenges for individualized treatment planning. Recent progress in artificial intelligence (AI), computational oncology, systems biology, multimodal biomedical data integration, and predictive analytics has accelerated the development of digital twins as an innovative framework for precision gynecologic oncology. A digital twin is a continuously evolving virtual representation of an individual patient that integrates clinical, radiological, pathological, molecular, genomic, physiological, and longitudinal health information to simulate disease progression, therapeutic response, treatment toxicity, and long-term outcomes. Unlike conventional predictive models that rely on static datasets, digital twins dynamically update as new patient data become available, enabling adaptive clinical decision-making throughout diagnosis, treatment, surveillance, and survivorship. Emerging technologies including deep learning, foundation models, graph neural networks, reinforcement learning, explainable AI, and generative AI have significantly enhanced the predictive capabilities of gynecologic oncology digital twins. These systems demonstrate promising applications in early diagnosis, radiogenomics, personalized surgery, fertility preservation, adaptive radiation therapy, immunotherapy prediction, and precision drug development. This review provides a comprehensive overview of the evolution, computational foundations, clinical applications, and future prospects of digital twins for personalized management of gynecologic malignancies while highlighting current challenges related to data interoperability, regulatory validation, ethical governance, and clinical implementation. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-03-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/51 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 3, March 2026; 11-21 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/51/43 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/52 2026-07-10T06:39:28Z files:ART

Next-Generation Healthcare: Artificial Intelligence, Digital Twins, and Multimodal Precision Medicine Mr. Abhishek Tandon Dr. Ritu Malhotra Dr. Hemant Chaturvedi Mrs. Sneha Venkatesan Dr. Nikhil Bhardwaj Dr. Swati Kulshrestha Artificial intelligence, Digital twins, Precision medicine, Multimodal learning, Personalized healthcare, Foundation models, Computational medicine, Medical imaging, Clinical decision support, Digital health. Background: Healthcare is undergoing a profound transformation driven by advances in artificial intelligence (AI), digital twin technology, multimodal data integration, and precision medicine. Conventional healthcare systems primarily rely on population-based treatment guidelines and episodic clinical assessments, which often fail to capture the biological complexity and dynamic nature of individual patients. Recent developments in computational modeling have introduced digital twins as continuously evolving virtual representations of patients that integrate clinical records, medical imaging, genomic sequencing, laboratory investigations, physiological monitoring, wearable devices, and environmental information into adaptive computational models capable of simulating disease progression and therapeutic response. Simultaneously, multimodal artificial intelligence enables the integration of heterogeneous biomedical information from radiology, pathology, genomics, proteomics, metabolomics, electronic health records, and real-time biosensors to generate comprehensive patient-specific insights. These technologies collectively support early diagnosis, individualized risk prediction, treatment optimization, preventive medicine, remote monitoring, and lifelong health management. Recent advances in deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, and generative artificial intelligence have further accelerated the development of intelligent healthcare ecosystems capable of continuously learning from multimodal biomedical data. Despite remarkable progress, significant challenges remain regarding data interoperability, computational complexity, explainability, cybersecurity, ethical governance, regulatory validation, and equitable implementation. This review provides a comprehensive overview of artificial intelligence, digital twins, and multimodal precision medicine as foundational technologies driving the next generation of intelligent healthcare systems. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-03-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/52 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 3, March 2026; 22-31 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/52/44 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/53 2026-07-10T06:47:39Z files:ART

Artificial Intelligence–Driven Clinical Decision Support Across Oncology, Cardiology, Pediatrics, and Women's Health Mr. Praveen Joshi Dr. Monica Arora Dr. Siddharth Rao Dr. Aishwarya Nambiar Dr. Rohan Kulshreshtha Artificial intelligence, Clinical decision support, Oncology, Cardiology, Pediatrics, Women's health, Precision medicine, Machine learning, Deep learning, Healthcare informatics. Background: Artificial intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare, fundamentally reshaping clinical decision-making across multiple medical specialties. Rapid advances in machine learning, deep learning, natural language processing, computer vision, multimodal learning, and foundation models have enabled AI systems to integrate heterogeneous biomedical information from electronic health records, medical imaging, laboratory investigations, genomic sequencing, physiological monitoring, wearable devices, and clinical documentation into comprehensive decision-support platforms. Unlike conventional rulebased clinical systems, AI-driven clinical decision support (CDS) continuously learns from large-scale multimodal datasets, enabling personalized diagnosis, prognostic prediction, therapeutic optimization, risk stratification, medication management, and long-term disease monitoring. Significant clinical applications have been demonstrated in oncology, cardiology, pediatrics, and women's health, where AI assists clinicians in disease detection, treatment planning, preventive care, intensive monitoring, and individualized patient management. Recent advances in transformer architectures, graph neural networks, reinforcement learning, explainable artificial intelligence, federated learning, and generative AI have further enhanced the capability, scalability, and interpretability of intelligent clinical decision-support systems. Nevertheless, widespread implementation remains challenged by data interoperability, algorithmic bias, cybersecurity, regulatory validation, ethical governance, and clinician acceptance. This review provides a comprehensive overview of artificial intelligence-driven clinical decision support across oncology, cardiology, pediatrics, and women's health, highlighting current technologies, clinical applications, implementation challenges, and future opportunities for precision healthcare. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-03-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/53 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 3, March 2026; 32-41 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/53/45 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/54 2026-07-10T06:54:13Z files:ART

Precision Medicine in Ovarian, Cervical, and Endometrial Cancer Using Artificial Intelligence Mr. Nishant Sinha Mr. Harish Kumar Dr. Mehul Shah Dr. Poonam Yadav Precision medicine, Artificial intelligence, Ovarian cancer, Cervical cancer, Endometrial cancer, Gynecologic oncology, Machine learning, Radiomics, Computational pathology, Personalized medicine. Background: Precision medicine has transformed the management of gynecologic malignancies by enabling individualized diagnosis, prognostic assessment, therapeutic selection, and long-term disease monitoring based on each patient's unique molecular and clinical characteristics. Ovarian, cervical, and endometrial cancers exhibit remarkable biological heterogeneity arising from distinct genomic alterations, epigenetic modifications, immune interactions, hormonal influences, and tumor microenvironment dynamics. Conventional treatment strategies based primarily on histopathological classification and population-level clinical evidence frequently fail to capture this complexity, resulting in considerable variability in therapeutic response and patient outcomes. Recent advances in artificial intelligence (AI), multimodal learning, computational pathology, radiomics, radiogenomics, and molecular oncology have substantially enhanced precision medicine by integrating medical imaging, genomic sequencing, transcriptomics, proteomics, digital pathology, laboratory investigations, electronic health records, and longitudinal clinical information into comprehensive computational models. Machine learning, deep learning, transformer architectures, graph neural networks, foundation models, reinforcement learning, and explainable artificial intelligence now support early cancer detection, molecular classification, treatment optimization, prediction of therapeutic response, recurrence monitoring, and survivorship care across gynecologic oncology. Despite remarkable progress, challenges remain regarding data interoperability, model transparency, ethical governance, cybersecurity, regulatory approval, and equitable clinical implementation. This review provides a comprehensive overview of artificial intelligence-driven precision medicine in ovarian, cervical, and endometrial cancer, highlighting computational foundations, clinical applications, emerging technologies, and future perspectives for personalized gynecologic oncology. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-03-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/54 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 3, March 2026; 42-51 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/54/46 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/55 2026-07-10T07:17:54Z files:ART

Artificial Intelligence in Pediatric Oncology: Toward Personalized Childhood Cancer Care Dr. Arjun Malhotra Mrs. Kavya Rao Dr. Deepak Mishra Mr. Harish Kumar Dr. Pooja Singh Artificial intelligence, Pediatric oncology, Childhood cancer, Precision medicine, Machine learning, Deep learning, Computational pathology, Medical imaging, Personalized medicine, Clinical decision support. Pediatric oncology has witnessed remarkable advances over the past several decades, resulting in significant improvements in survival for many childhood cancers. Nevertheless, cancer remains one of the leading causes of disease-related mortality among children worldwide, and survivors frequently experience long-term treatment-related complications affecting physical, cognitive, endocrine, cardiovascular, and psychosocial health. The extraordinary biological diversity of childhood malignancies, together with age-dependent physiology, genetic predisposition, developmental considerations, and variable therapeutic responses, necessitates highly individualized approaches to diagnosis and treatment. Recent developments in artificial intelligence (AI), computational oncology, multimodal data integration, and precision medicine have introduced transformative opportunities for personalized childhood cancer care. Artificial intelligence integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, laboratory investigations, electronic health records, physiological monitoring, and longitudinal clinical information into comprehensive computational models capable of supporting diagnosis, molecular classification, prognostic prediction, treatment optimization, toxicity assessment, recurrence monitoring, and survivorship management. Advances in deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, and explainable artificial intelligence have further accelerated clinical implementation across pediatric oncology. Despite remarkable progress, challenges remain regarding limited pediatric datasets, data interoperability, algorithmic fairness, ethical governance, cybersecurity, regulatory validation, and equitable healthcare access. This review provides a comprehensive overview of artificial intelligence in pediatric oncology, highlighting computational foundations, current clinical applications, emerging innovations, implementation challenges, and future perspectives toward personalized childhood cancer care. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-04-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/55 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 4, April 2026; 10-19 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/55/47 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/56 2026-07-10T07:27:51Z files:ART

Precision Cardio-Oncology: Digital Twins and AI for Personalized Cardiovascular Risk Assessment Dr. Pooja Singh Dr. Arjun Malhotra Mrs. Kavya Rao Dr. Deepak Mishra Mr. Harish Kumar Cardio-oncology, Artificial intelligence, Digital twins, Precision medicine, Cardiovascular toxicity, Machine learning, Personalized healthcare, Echocardiography, Cardiac imaging, Clinical decision support. Background: Cardio-oncology has emerged as a rapidly evolving multidisciplinary specialty focused on preventing, detecting, and managing cardiovascular complications associated with cancer and its treatment. Although remarkable advances in chemotherapy, targeted therapies, immunotherapy, and radiation therapy have significantly improved cancer survival, cardiovascular disease has become one of the leading causes of long-term morbidity and mortality among cancer survivors. The complex interactions among tumor biology, cardiovascular physiology, therapeutic exposure, genetic susceptibility, immune responses, and patient-specific clinical factors necessitate individualized approaches to cardiovascular risk assessment and management. Recent developments in artificial intelligence (AI), digital twin technology, multimodal data integration, and computational medicine have introduced transformative opportunities for precision cardio-oncology. Digital twins are continuously evolving virtual representations of individual patients that integrate cardiac imaging, electrocardiography, genomic sequencing, laboratory biomarkers, wearable devices, electronic health records, cancer treatment history, and longitudinal clinical information to simulate cardiovascular health, predict treatment-related toxicity, and optimize personalized therapeutic strategies. Advances in machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, and explainable artificial intelligence have significantly enhanced cardiovascular prediction, early diagnosis, treatment optimization, and survivorship management. Despite remarkable progress, challenges remain regarding data interoperability, computational complexity, ethical governance, cybersecurity, regulatory validation, and large-scale clinical implementation. This review provides a comprehensive overview of artificial intelligence and digital twin technologies in precision cardio-oncology, highlighting computational foundations, clinical applications, emerging innovations, and future perspectives for personalized cardiovascular risk assessment in patients with cancer. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-04-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/56 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 4, April 2026; 20-29 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/56/48 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/57 2026-07-10T07:32:07Z files:ART

Cognitive Radiology: Artificial Intelligence in Oncology Imaging and Personalized Cancer Care Somashekhar SP Cognitive radiology, Artificial intelligence, Oncology imaging, Radiomics, Radiogenomics, Precision medicine, Medical imaging, Deep learning, Personalized cancer care, Clinical decision support. Background: Medical imaging plays a central role throughout the cancer care continuum, supporting early detection, diagnosis, staging, treatment planning, therapeutic monitoring, and long-term surveillance. However, the increasing complexity and volume of imaging data generated by computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, mammography, and hybrid imaging technologies have exceeded the interpretive capacity of conventional radiological workflows. Recent advances in artificial intelligence (AI), computational imaging, radiomics, radiogenomics, multimodal learning, and cognitive radiology have transformed oncology imaging by enabling automated image interpretation, quantitative biomarker extraction, molecular prediction, and personalized clinical decision support. Cognitive radiology refers to the integration of AI with advanced medical imaging to emulate aspects of expert radiological reasoning through continuous analysis of imaging, pathology, genomics, laboratory investigations, electronic health records, and longitudinal clinical information. Machine learning, deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, and explainable artificial intelligence have significantly enhanced tumor detection, lesion characterization, treatment response assessment, adaptive imaging, and personalized cancer management. Despite remarkable progress, challenges remain regarding data standardization, interoperability, computational scalability, algorithmic transparency, regulatory validation, cybersecurity, and equitable clinical implementation. This review provides a comprehensive overview of cognitive radiology and artificial intelligence in oncology imaging, highlighting computational foundations, current clinical applications, emerging innovations, and future perspectives for personalized cancer care. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-04-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/57 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 4, April 2026; 30-39 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/57/49 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/59 2026-07-25T06:24:21Z files:ART

AI and Precision Medicine in Breast Cancer: Towards Truly Personalized Care Somashekhar SP Breast cancer, Artificial intelligence, Precision medicine, Mammography, Digital pathology, Radiomics, Personalized medicine, Machine learning, Oncology imaging, Clinical decision support. Background: Breast cancer remains the most frequently diagnosed malignancy and one of the leading causes of cancer-related mortality among women worldwide despite remarkable advances in screening, molecular diagnostics, targeted therapies, and personalized treatment strategies. The extraordinary biological heterogeneity of breast cancer, characterized by diverse molecular subtypes, genomic alterations, tumor microenvironment interactions, hormonal influences, and immune responses, presents significant challenges to conventional population-based treatment approaches. Recent developments in artificial intelligence (AI), computational oncology, multimodal data integration, and precision medicine have transformed breast cancer management by enabling individualized diagnosis, prognostic prediction, therapeutic optimization, recurrence monitoring, and survivorship care. Artificial intelligence integrates mammography, digital breast tomosynthesis, magnetic resonance imaging, ultrasound, digital pathology, genomic sequencing, transcriptomics, proteomics, laboratory biomarkers, electronic health records, and longitudinal clinical information into comprehensive computational models capable of supporting personalized clinical decision-making. Advances in machine learning, deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, and explainable artificial intelligence have significantly enhanced breast cancer detection, molecular classification, treatment selection, prediction of therapeutic response, toxicity assessment, and long-term disease surveillance. Despite remarkable progress, important challenges remain regarding data interoperability, computational scalability, ethical governance, cybersecurity, regulatory validation, and equitable clinical implementation. This review provides a comprehensive overview of artificial intelligence-driven precision medicine in breast cancer, highlighting computational foundations, current clinical applications, emerging innovations, and future perspectives toward truly personalized breast cancer care. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/59 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 5, May 2026; 1-10 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/59/50 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/60 2026-07-25T06:25:04Z files:ART

Digital Twins in Renal Cell Carcinoma: Toward Personalized Therapy Dr. Swati Joshi Mr. Vikas Yadav Miss. Bindu Gajanan Dr. Vishal Rastogi Digital twins, Renal cell carcinoma, Artificial intelligence, Precision medicine, Kidney cancer, Radiogenomics, Computational oncology, Personalized therapy, Machine learning, Clinical decision support. Background: Renal cell carcinoma (RCC) is the most common primary malignancy of the kidney and accounts for approximately 90% of all renal cancers. Despite remarkable advances in molecular diagnostics, targeted therapies, immune checkpoint inhibitors, minimally invasive surgery, and precision medicine, considerable heterogeneity in tumor biology and therapeutic response continues to challenge individualized patient management. Renal cell carcinoma encompasses multiple histological and molecular subtypes, each characterized by distinct genomic alterations, metabolic reprogramming, immune microenvironment interactions, angiogenesis, and variable sensitivityto systemic therapies. Recent developments in artificial intelligence (AI), digital twin technology, computational oncology, and multimodal biomedical data integration have introduced transformative opportunities for personalized renal cancer care. A digital twin is a continuously evolving virtual representation of an individual patient that integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, laboratory biomarkers, physiological monitoring, electronic health records, and longitudinal clinical information to simulate tumor progression and therapeutic response. Unlike conventional predictive models based on static datasets, digital twins continuously update as new clinical information becomes available, enabling individualized diagnosis, prognostic prediction, treatment optimization, toxicity assessment, recurrence monitoring, and survivorship management. Advances in machine learning, deep learning, transformer architectures, graph neural networks, foundation models, reinforcement learning, and explainable artificial intelligence have significantly accelerated the development of digital twin technology in renal oncology. These intelligent computational systems demonstrate growing applications in diagnostic imaging, radiogenomics, immunotherapy prediction, nephronsparing surgery, adaptive systemic therapy, clinical trial optimization, and precision drug development. Nevertheless, important challenges remain regarding data interoperability, computational scalability, ethical governance, cybersecurity, regulatory validation, and widespread clinical implementation. This review provides a comprehensive overview of digital twin technologies in renal cell carcinoma, highlighting computational foundations, clinical applications, emerging innovations, and future perspectives toward truly personalized therapy. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/60 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 5, May 2026; 11-20 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/60/51 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/61 2026-07-25T06:25:54Z files:ART

Emerging Technologies Shaping the Future of Precision Oncology Somashekhar SP Precision oncology, Artificial intelligence, Digital twins, Radiomics, Radiogenomics, Spatial multi-omics, Liquid biopsy, Personalized medicine, Computational oncology, Clinical decision support. Background: Precision oncology has transformed modern cancer care by enabling individualized diagnosis, molecular characterization, therapeutic selection, and longitudinal disease monitoring based on the unique biological characteristics of each patient and tumor. Despite remarkable advances in genomic medicine, targeted therapies, immunotherapy, and molecular diagnostics, considerable heterogeneity in tumor biology, therapeutic response, and resistance mechanisms continues to challenge conventional treatment strategies. Recent developments in artificial intelligence (AI), digital twin technology, multimodal learning, radiomics, radiogenomics, computational pathology, spatial multi-omics, liquid biopsy, wearable technologies, and advanced computational modeling are collectively redefining the future of personalized cancer care. These emerging technologies integrate radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, electronic health records, physiological monitoring, and longitudinal clinical information into adaptive computational ecosystems capable of supporting diagnosis, prognostic prediction, treatment optimization, toxicity assessment, recurrence surveillance, and survivorship management. Advances in deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, explainable artificial intelligence, federated learning, and generative AI have significantly accelerated precision oncology research and clinical implementation. Nevertheless, important scientific, ethical, computational, regulatory, and socioeconomic challenges remain before widespread clinical adoption becomes feasible. This review provides a comprehensive overview of emerging technologies shaping the future of precision oncology, highlighting computational foundations, translational applications, implementation challenges, and future perspectives toward truly personalized cancer care. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/61 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 5, May 2026; 21-30 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/61/52 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/62 2026-07-10T08:58:23Z files:ART

Artificial Intelligence in Precision Oncology: Current Advances, Clinical Applications, and Future Directions Dr. Ananya Iyer Mr. Harish Kumar Dr. Pooja Singh Dr. Arjun Malhotra Mrs. Kavya Rao Dr. Deepak Mishra Artificial intelligence, Precision oncology, Machine learning, Deep learning, Digital pathology, Radiomics, Clinical oncology, Cancer diagnosis, Personalized medicine, Multimodal learning Background: Precision oncology has transformed modern cancer management by integrating molecular profiling, advanced imaging, and individualized therapeutic strategies to improve clinical outcomes. However, the increasing complexity of multidimensional clinical, radiological, pathological, genomic, transcriptomic, and real-world patient data has created significant analytical challenges that exceed traditional computational approaches. Artificial intelligence (AI) has emerged as a transformative technology capable of extracting clinically meaningful patterns from heterogeneous biomedical datasets and supporting personalized cancer diagnosis, prognosis, treatment selection, and disease monitoring. Recent advances in machine learning, deep learning, transformer architectures, multimodal learning, explainable AI, and large language models have substantially expanded AI applications across oncology. AI systems are increasingly utilized in digital pathology, radiology, radiomics, genomics, immunotherapy prediction, biomarker discovery, clinical decision support, and drug development. These technologies facilitate earlier cancer detection, improved tumor characterization, accurate risk stratification, prediction of therapeutic response, and optimization of precision treatment strategies. Furthermore, integration of multimodal datasets enables comprehensive patient-specific models capable of supporting individualized clinical decisions. Despite remarkable progress, important challenges remain regarding data quality, algorithmic bias, interpretability, privacy protection, regulatory approval, and clinical implementation. This review summarizes recent advances in artificial intelligence for precision oncology, discusses current clinical applications across multiple oncology domains, highlights existing limitations, and explores future opportunities for AI-driven personalized cancer care. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-07-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/62 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 6, June 2026; 1-10 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/62/53 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/63 2026-07-10T08:58:22Z files:ART

Digital Pathology in Cancer Medicine: Applications, Benefits, and Future Directions Dr. Mehul Shah Dr. Poonam Yadav Mr. Nishant Sinha Mr. Harish Kumar Mrs. Kavya Rao Dr. Deepak Mishra Digital pathology, Cancer medicine, Computational pathology, Artificial intelligence, Whole-slide imaging, Precision oncology, Deep learning, Histopathology, Biomarkers, Precision medicine. Background: Digital pathology has emerged as one of the most transformative innovations in modern cancer medicine, fundamentally changing how pathological specimens are analyzed, interpreted, archived, and integrated into precision oncology. Traditional microscopy-based pathology has served as the gold standard for cancer diagnosis for more than a century; however, increasing cancer incidence, growingdiagnostic complexity, and the rapid expansion of molecular medicine have created substantial challenges related to workload, reproducibility, and diagnostic consistency. Digital pathology addresses these limitations by converting conventional glass slides into high-resolution whole-slide images that can be analyzed using advanced computational algorithms, artificial intelligence (AI), and deep learning technologies. The integration of digital pathology with radiology, genomics, transcriptomics, proteomics, and electronic health records has facilitated the development of comprehensive multimodal diagnostic platforms capable of improving tumor classification, biomarker identification, prognostic prediction, and therapeutic decision-making. Recent advances in machine learning, transformerbased architectures, computational image analysis, and foundation models have further accelerated the role of digital pathology in precision oncology by enabling automated tissue segmentation, molecular prediction, tumor microenvironment characterization, and prediction of treatment response. Digital pathology also supports remote diagnostics, telepathology, multidisciplinary collaboration, educational training, and large-scale research through efficient digital data sharing and centralized image repositories. Despite its remarkable advantages, challenges including image standardization, data storage requirements, algorithm interpretability, cybersecurity, regulatory approval, and integration into routine clinical workflows remain significant barriers to widespread implementation. This review provides a comprehensive overview of digital pathology technologies, clinical applications, advantages, current limitations, and future directions in cancer medicine while highlighting the expanding role of computational pathology in advancing personalized oncology and precision healthcare. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-06-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/63 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 6, June 2026; 11-20 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/63/54 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/64 2026-07-11T09:11:56Z files:ART

Artificial Intelligence in Oncology Clinical Decision Support: From Diagnosis to Precision Cancer Care Dr. Siddharth Rao Dr. Siddharth Rao Mr. Praveen Joshi Dr. Monica Arora Dr. Rohan Kulshreshtha Clinical decision support systems, Artificial intelligence, Oncology, Precision medicine, Machine learning, Deep learning, Digital pathology, Radiology, Electronic health records, Cancer informatics. Background: Clinical Decision Support Systems (CDSS) have become an integral component of modern oncology by assisting clinicians in diagnosis, treatment planning, prognostic assessment, therapeutic monitoring, and personalized patient management. The increasing complexity of cancer care, driven by rapidly expanding genomic information, advanced imaging modalities, biomarker discovery, immunotherapy, and precision medicine, has created an unprecedented demand for intelligent computational tools capable of integrating heterogeneous clinical data. Artificial intelligence (AI), particularly machine learning, deep learning, natural language processing, reinforcement learning, and multimodal foundation models, has significantly enhanced the capabilities of oncology CDSS by enabling accurate prediction, automated interpretation, risk stratification, and evidence-based clinical recommendations. AI-driven CDSS can integrate radiological imaging, digital pathology, genomic sequencing, electronic health records, laboratory investigations, wearable sensor data, and published clinical evidence to support multidisciplinary decision-making throughout the cancer care continuum. Recent advances have demonstrated substantial improvements in tumor detection, molecular subtype prediction, treatment selection, immunotherapy response prediction, toxicity assessment, survival estimation, and clinical workflow optimization. Nevertheless, important challenges remain regarding model interpretability, algorithmic bias, regulatory approval, cybersecurity, interoperability, and ethical implementation. This review discusses the evolution, computational architecture, major clinical applications, current limitations, and future perspectives of artificial intelligence-powered clinical decision support systems in oncology. It also highlights how intelligent CDSS are reshaping precision cancer care by improving diagnostic accuracy, therapeutic personalization, and clinical efficiency while supporting evidence-based oncology practice. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-06-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/64 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 6, June 2026; 21-28 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/64/55 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/65 2026-07-10T08:58:22Z files:ART

Artificial Intelligence in Modern Adult and Pediatric Cancer Care: Opportunities, Challenges, and Future Perspectives Dr. Nikhil Bhardwaj Dr. Swati Kulshrestha Mr. Abhishek Tandon Dr. Ritu Malhotra Dr. Hemant Chaturvedi Mrs. Sneha Venkatesan Artificial intelligence, Cancer care, Precision oncology, Pediatric oncology, Adult oncology, Machine learning, Deep learning, Digital pathology, Radiomics, Clinical decision support. Background: Cancer remains one of the leading causes of morbidity and mortality worldwide, affecting both adult and pediatric populations through diverse biological mechanisms, clinical presentations, and therapeutic responses [1]. While remarkable advances in molecular diagnostics, targeted therapies, immunotherapy, and precision medicine have significantly improved survival rates, considerable challenges continue to limit optimal cancer care. Delayed diagnosis, tumor heterogeneity, treatment resistance, adverse drug reactions, healthcare disparities, and increasing clinical workload continue to affect patient outcomes across multiple cancer types [2]. Artificial intelligence (AI) has emerged as a transformative technology capable of addressing many of these limitations through advanced computational analysis of complex biomedical data. Modern AI systems integrate machine learning, deep learning, natural language processing, computer vision, generative AI, and multimodal analytics to improve cancer screening, diagnosis, prognostic assessment, therapeutic planning, treatment monitoring, and survivorship care [3]. AI applications now extend across medical imaging, digital pathology, genomics, radiomics, clinical decision support systems, robotic surgery, drug discovery, and personalized oncology. In pediatric oncology, AI offers additional opportunities to enhance early diagnosis of rare childhood malignancies, optimize treatment protocols, minimize long-term toxicities, and improve quality of life for childhood cancer survivors [4]. Nevertheless, significant barriers remain regarding algorithmic bias, explainability, regulatory approval, cybersecurity, data privacy, interoperability, and ethical implementation. This review discusses the evolving role of artificial intelligence across adult and pediatric oncology, highlighting current applications, clinical benefits, existing limitations, and future directions that may redefine precision cancer care over the coming decades. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-06-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/65 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 6, June 2026; 29-38 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/65/56 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/66 2026-07-11T10:14:41Z files:ART

Deep Learning in Oncology: Applications in Diagnosis, Prognosis, and Personalized Treatment Dr. Rahul Srivastava Dr. Komal Desai Mr. Ajay Kuldeep Dr. Divya Menon Mrs. Bhavana Rao Deep learning, Oncology, Artificial intelligence, Precision medicine, Digital pathology, Medical imaging, Radiomics, Cancer diagnosis, Personalized treatment, Prognosis. Background: Cancer remains one of the leading causes of morbidity and mortality worldwide, accounting for millions of new diagnoses and deaths each year. The remarkable biological heterogeneity of malignant tumors presents substantial challenges for accurate diagnosis, prognosis, therapeutic decision-making, and long-term disease monitoring. Conventional oncology relies on histopathology, radiological imaging, molecular diagnostics, and clinical evaluation; however, the growing complexity and volume of cancer-related data often exceed the capabilities of traditional analytical methods. Deep learning, a rapidly evolving branch of artificial intelligence, has emerged as a transformative technology capable of extracting high-dimensional patterns from complex biomedical datasets with minimal manual feature engineering. Convolutional neural networks, recurrent neural networks, graph neural networks, vision transformers, and hybrid deep learning architectures have demonstrated outstanding performance across multiple oncology applications, including tumor detection, image segmentation, cancer classification, survival prediction, genomic analysis, treatment response assessment, drug discovery, and precision medicine. These models enable integration of multimodal information derived from digital pathology, radiological imaging, genomic sequencing, transcriptomics, proteomics, and electronic health records to facilitate personalized therapeutic strategies. Despite their promising clinical utility, challenges related to interpretability, data heterogeneity, algorithmic bias, computational requirements, and regulatory approval continue to impede widespread clinical implementation. This review comprehensively discusses the principles, architectures, clinical applications, advantages, limitations, and future directions of deep learning in oncology, emphasizing its growing role in advancing precision cancer care and improving patient outcomes through intelligent data-driven decision support. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-06-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/66 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 6, June 2026; 39-47 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/66/57 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijmrbpdms.org:article/67 2026-07-24T06:45:35Z files:ART

Precision Oncology Beyond Genomics: The Integration of Multi-Omics and Artificial Intelligence Dr. Bhavna Rao Dr. Anil Thomas Mrs. Komal Shah Dr. Dinesh Kulkarni Mr. Rakesh Verma Precision oncology, Multi-omics, Artificial intelligence, Genomics, Transcriptomics, Proteomics, Metabolomics, Machine learning, Precision medicine, Systems biology. Precision oncology has traditionally focused on genomic profiling to guide personalized cancer diagnosis and therapy. Although genomic medicine has significantly improved the identification of actionable mutations and targeted treatment strategies, genomic information alone cannot fully explain the remarkable biological complexity and clinical heterogeneity of cancer. Tumor behavior is influenced by multiple interconnected molecular layers, including epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics, and immunomics, which collectively regulate disease initiation, progression, therapeutic response, and resistance. The emergence of multi-omics technologies, combined with advances in artificial intelligence (AI), has enabled comprehensive characterization of cancer biology through integration of heterogeneous molecular and clinical datasets. Machine learning, deep learning, graph neural networks, transformer architectures, multimodal learning, and foundation models are increasingly capable of analyzing high-dimensional omics data alongside radiological imaging, digital pathology, electronic health records, and laboratory biomarkers. These computational approaches support improved cancer diagnosis, prognostic prediction, biomarker discovery, therapeutic selection, immunotherapy response prediction, drug discovery, and precision clinical decision-making. Despite remarkable progress, challenges remain regarding data harmonization, interoperability, computational complexity, explainability, ethical governance, regulatory validation, and equitable clinical implementation. This review provides a comprehensive overview of multi-omics integration and artificial intelligence in precision oncology, highlighting current applications, technological innovations, and future directions toward truly individualized cancer care.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/67 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 1-7 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/67/58 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/69 2026-07-24T06:45:35Z files:ART

Next-Generation Precision Oncology: Emerging Technologies Reshaping Cancer Care Dr. Tanvi Ghosh Dr. Sameer Bansal Mrs. Pooja Kulshreshtha Dr. Nitin Agarwa Mr. Ashwin Rao Precision oncology, Artificial intelligence, Multi-omics, Digital pathology, Radiomics, Liquid biopsy, Spatial transcriptomics, Foundation models, Precision medicine, Cancer informatics. Precision oncology is undergoing a profound transformation driven by rapid advances in artificial intelligence (AI), multi-omics technologies, digital pathology, radiomics, liquid biopsy, spatial biology, single-cell sequencing, wearable health technologies, and computational medicine. Traditional precision oncology has largely focused on genomic profiling to guide targeted therapies; however, emerging technologies now enable comprehensive characterization of tumor biology through integration of molecular, cellular, imaging, clinical, and real-world patient data. Machine learning, deep learning, transformer architectures, graph neural networks, multimodal learning, foundation models, and generative AI are increasingly capable of analyzing these heterogeneous datasets to improve cancer diagnosis, prognostic prediction, biomarker discovery, treatment optimization, immunotherapy selection, adaptive radiation planning, and drug development. Simultaneously, innovations such as spatial transcriptomics, digital twins, robotic surgery, cloud computing, and federated learning are creating intelligent clinical ecosystems that continuously learn from evolving patient data. These advances have the potential to improve diagnostic accuracy, personalize therapy, reduce treatment-related toxicity, accelerate clinical research, and enhance healthcare efficiency. Nevertheless, widespread implementation remains challenged by data standardization, interoperability, computational complexity, cybersecurity, explainability, ethical governance, regulatory validation, and equitable access to advanced technologies. This review provides a comprehensive overview of next-generation precision oncology, highlighting emerging technologies that are reshaping cancer care and discussing future directions toward predictive, preventive, adaptive, and highly personalized medicine.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/69 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 8-14 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/69/60 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/71 2026-07-24T06:45:35Z files:ART

Precision Cancer Medicine in the Era of Artificial Intelligence Dr. Swati Joshi Dr. Prakash Menon Mrs. Neelam Sinha Dr. Chetan Kulkarni Mr. Himanshu Das Precision cancer medicine, Artificial intelligence, Precision oncology, Machine learning, Deep learning, Digital pathology, Multi-omics, Clinical decision support, Radiomics, Personalized medicine. Precision cancer medicine has entered a new era driven by rapid advances in artificial intelligence (AI), computational biology, and high-throughput molecular technologies. While traditional precision oncology primarily relied on genomic profiling to guide targeted therapies, contemporary cancer care increasingly integrates multidimensional biomedical information—including radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, liquid biopsy, electronic health records, and real-world clinical data—to provide individualized diagnosis and treatment. Artificial intelligence has emerged as the central enabling technology capable of interpreting these complex datasets through machine learning, deep learning, computer vision, natural language processing, graph neural networks, multimodal learning, and foundation models. AI-powered systems are transforming every stage of cancer management by improving early diagnosis, prognostic prediction, biomarker discovery, treatment selection, immunotherapy response assessment, adaptive radiation planning, surgical decision-making, drug discovery, and survivorship care. Emerging innovations including generative AI, digital twins, federated learning, explainable AI, single-cell sequencing, spatial biology, and cloud-based intelligent healthcare systems further expand the scope of personalized oncology. Despite these remarkable advances, important scientific, ethical, technical, and regulatory challenges remain regarding data quality, interoperability, algorithmic bias, cybersecurity, transparency, validation, and equitable clinical implementation. This review provides a comprehensive overview of precision cancer medicine in the era of artificial intelligence, highlighting technological foundations, current clinical applications, emerging innovations, and future perspectives that are reshaping modern oncology.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/71 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 15-21 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/71/61 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/72 2026-07-24T06:45:35Z files:ART

Precision Oncology 2.0: From Molecular Insights to Personalized Therapeutics Dr. Rekha Srinivasan Dr. Ajay Sharma Mrs. Monika Bedi Dr. Vinod Ramesh Mr. Suresh Patnaik Precision oncology, Artificial intelligence, Personalized therapeutics, Multi-omics, Precision medicine, Digital pathology, Radiomics, Machine learning, Foundation models, Cancer informatics. Precision oncology has evolved beyond conventional genomic medicine into an integrated, data-driven discipline that combines molecular biology, artificial intelligence (AI), multi-omics technologies, advanced imaging, and computational medicine to deliver highly personalized cancer care. While early precision oncology primarily focused on identifying actionable genomic mutations for targeted therapy, emerging technologies now enable comprehensive characterization of tumor biology through the integration of genomics, epigenomics, transcriptomics, proteomics, metabolomics, radiomics, digital pathology, liquid biopsy, and real-world clinical data. Artificial intelligence has become the computational engine that transforms these complex datasets into clinically actionable knowledge through machine learning, deep learning, multimodal learning, graph neural networks, transformer architectures, and foundation models. These innovations are improving cancer diagnosis, prognostic prediction, biomarker discovery, therapeutic selection, immunotherapy response assessment, adaptive radiation planning, drug development, and long-term survivorship management. In addition, advances in spatial biology, single-cell sequencing, digital twins, explainable AI, federated learning, and intelligent clinical decision-support systems are redefining precision oncology as a continuously learning healthcare ecosystem. Despite remarkable progress, challenges remain regarding data harmonization, interoperability, computational scalability, cybersecurity, transparency, ethical governance, regulatory approval, and equitable clinical implementation. This review discusses the evolution of Precision Oncology 2.0, highlighting emerging technologies that are transforming molecular insights into individualized therapeutic strategies for the next generation of cancer care.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/72 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 22-28 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/72/62 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/73 2026-07-24T06:45:35Z files:ART

Reimagining Precision Oncology Through AI and Computational Medicine Dr. Ishita Banerjee Dr. Naveen Chandra Mrs. Lakshmi Nair Dr. Faisal Khan Mr. Yogesh Pawar Precision oncology, Artificial intelligence, Computational medicine, Machine learning, Precision medicine, Multi-omics, Digital pathology, Radiomics, Clinical decision support, Cancer informatics. Precision oncology is undergoing a transformative evolution driven by artificial intelligence (AI), computational medicine, and the rapid expansion of multidimensional biomedical data. While conventional precision medicine has largely focused on genomic profiling and targeted therapeutics, emerging computational approaches now integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, liquid biopsy, wearable health technologies, electronic health records, and real-world clinical data into comprehensive patient-specific models. Machine learning, deep learning, natural language processing, computer vision, graph neural networks, multimodal learning, transformer architectures, and foundation models have significantly enhanced the ability to interpret these heterogeneous datasets and generate clinically actionable insights. AI-powered computational systems are increasingly supporting early cancer detection, molecular classification, prognostic prediction, biomarker discovery, therapeutic optimization, immunotherapy response assessment, adaptive radiation planning, drug discovery, and intelligent clinical decision support. Emerging innovations including digital twins, spatial biology, federated learning, explainable AI, generative AI, and cloud-enabled healthcare ecosystems further expand the scope of precision oncology by enabling continuously learning models that adapt to evolving patient conditions. Despite remarkable progress, important challenges remain regarding data interoperability, model transparency, cybersecurity, ethical governance, regulatory validation, and equitable implementation. This review explores how artificial intelligence and computational medicine are reimagining precision oncology and shaping the future of individualized cancer care through data-driven clinical intelligence.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/73 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 29-35 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/73/63 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/74 2026-07-27T07:49:17Z files:ART

Craniofacial Anthropometry and Single Nucleotide Polymophysm as Differential Identification Tools for Idomas and Igedes Population of Benue State Boniface Adakole Onoja Kareem Suwebat Bidemi Alabi Ade Stephen Imam Aminu Samuel A. Agada Daniel E. Uti Ajao Moyosore Salihu Craniofacial indices; PAX3; BMP4; single nucleotide polymorphisms; forensic identification; anthropometry; Idoma; Igede; genetic variation; Nigeria. Background: Craniofacial characteristics vary significantly across human populations due to genetic and environmental influences. Although such variations have been widely studied globally, there is limited data on African populations. This study aimed to investigate craniofacial genetics as a tool for differential identification among the Idoma and Igede ethnic groups in Nigeria. Specifically, the study sought to: (i) calculate craniofacial indices from facial image measurements; (ii) identify single nucleotide polymorphisms (SNPs) in the PAX3 and BMP4 genes associated with facial morphology; and (iii) evaluate the relationship between these genetic variations and observable craniofacial traits. Methods:This study was conducted in two phases. The first phase involved an anthropometric survey of 3,000 randomly selected participants aged 15–30 years. Sample size was determined using Fisher’s formula for large populations (N = Z²Pq/d²). Demographic data were collected using semi-structured questionnaires. Standardized facial photographs were analyzed using Digimizer software to obtain measurements including facial height, bizygomatic width, nasal dimensions, intercanthal distance, and ear size. Data were analyzed using IBM SPSS version 23.0 at a 95% confidence interval. The second phase involved molecular analysis. DNA samples were collected via buccal swabs. Target regions of the PAX3 and BMP4 genes were amplified using polymerase chain reaction (PCR), followed by purification and sequencing. Sequences were compared with reference data from the NCBI human genome database. Results: Facial indices were 88.45 ± 4.72 (Idoma males) and 87.81 ± 4.55 (Igede males), and 85.67 ± 4.28 (Idoma females) and 86.12 ± 4.41 (Igede females). Nasal indices ranged from 92.34 ± 6.85 to 95.67 ± 6.78 across both groups. Canthal indices ranged from 37.45 ± 2.65 to 38.56 ± 2.72, while ear indices ranged from 55.98 ± 3.69 to 57.23 ± 3.94. Very long facial types predominated in both populations. Broad and flat nasal types were most common, while intermediate eye types (canthal index 37–46) and large ear sizes were predominant across sexes. Genetic analysis identified 34 nucleotide substitution mutations in the PAX3 gene and 22 in the BMP4 gene. Individuals carrying these mutations exhibited distinct craniofacial measurement patterns compared to the general population. Conclusion: The integration of craniofacial morphometric analysis with genetic profiling provides a valuable and reliable approach for forensic identification among Idoma and Igede populations. Incorporating genetic markers such as PAX3 and BMP4 into forensic protocols may enhance the identification of both living individuals and human remains. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-07-27 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/74 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 7, July 2026; 1-28 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/74/64 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/76 2026-08-06T15:37:02Z files:ART

Artificial Intelligence for Adaptive Precision Oncology: Continuous Learning Models Across the Cancer Care Continuum Dr.Harini Prasad Dr.Karthik Menon Mrs.Renu Thomas Dr.Abhishek Nair Dr.Deepa Shetty Mr.Mohit Rao Adaptive artificial intelligence, Precision oncology, Continuous learning, Machine learning, Digital twins, Foundation models, Computational oncology, Personalized medicine, Clinical decision support, Cancer informatics. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The dynamic nature of cancer biology, characterized by continuous genomic evolution, intratumoral heterogeneity, treatment-induced resistance, and complex interactions within the tumor microenvironment, requires adaptive clinical strategies that evolve throughout the patient's disease course. Recent advances in artificial intelligence (AI) have introduced continuous learning models capable of integrating radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical data into continuously evolving computational systems. Unlike conventional predictive models trained on static datasets, adaptive AI systems continuously learn from new patient information, enabling real-time refinement of diagnosis, prognostic prediction, therapeutic optimization, toxicity assessment, disease monitoring, and survivorship planning. Advances in machine learning, deep learning, reinforcement learning, transformer architectures, multimodal foundation models, graph neural networks, federated learning, digital twins, and agentic AI have accelerated development of intelligent oncology ecosystems capable of supporting personalized medicine across the entire cancer care continuum. Despite remarkable progress, significant challenges remain regarding continual learning, catastrophic forgetting, interoperability, explainability, cybersecurity, regulatory validation, ethical governance, and equitable implementation. This review provides a comprehensive overview of adaptive artificial intelligence in precision oncology, highlighting computational foundations, clinical applications, implementation challenges, and future perspectives for continuously learning cancer care systems.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/76 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 36-44 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/76/65 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/77 2026-08-06T15:48:00Z files:ART

Liquid Biopsy Intelligence: Integrating Circulating Biomarkers, Multi-Omics, and AI for Early Cancer Detection Dr.Anjali Kulkarni Dr.Naveen Kumar Dr.Rakesh Babu Mrs.Shweta Reddy Liquid biopsy, Artificial intelligence, Circulating tumor DNA, Multi-omics, Precision oncology, Early cancer detection, Circulating biomarkers, Machine learning, Precision medicine, Computational oncology. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. Early cancer detection remains one of the mosteffective strategies for improving patient survival, reducing treatment complexity, and enhancing long-term clinical outcomes.Conventional tissue biopsy, although considered the diagnostic gold standard, is invasive, may not adequately capture tumor heterogeneity, and often cannot be repeated frequently for longitudinal disease monitoring. Recent advances in liquid biopsy technologies have transformed precision oncology by enabling minimally invasive detection of circulating tumor-derived biomarkers including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free DNA (cfDNA), extracellular vesicles, exosomes, circulating RNA, proteins, metabolites, and tumor-educated platelets. Artificial intelligence (AI), multimodal learning, machine learning, deep learning, graph neural networks, transformer architectures, and foundation models have substantially enhanced interpretation of these highly complex circulating biomarker datasets by integrating liquid biopsy with multi-omics, radiological imaging, digital pathology, laboratory biomarkers, and longitudinal clinical information. These intelligent computational systems support early cancer detection, molecular characterization, prognostic prediction, treatment monitoring, minimal residual disease assessment, therapeutic optimization, and recurrence surveillance. Despite remarkable technological progress, important challenges remain regarding assay standardization, analytical sensitivity, computational scalability, explainability, regulatory validation, interoperability, and equitable implementation. This review provides a comprehensive overview of liquid biopsy intelligence in precision oncology, emphasizing computational principles, clinical applications, emerging innovations, and future perspectives for AI-driven early cancer detection.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/77 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 45-52 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/77/67 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/78 2026-08-06T15:58:34Z files:ART

Foundation AI Models for Predictive Immuno-Oncology: Personalizing Immunotherapy Through Multimodal Learning Dr.Ishaan Verma Dr.Monica George Dr.Rahul Iyer Mr.Nikhil Patil Dr.Farah Siddiqui Foundation models, Immuno-oncology, Artificial intelligence, Immunotherapy, Multimodal learning, Precision oncology, Tumor microenvironment, Computational pathology, Personalized medicine, Clinical decision support. Immunotherapy has revolutionized modern oncology by enabling durable clinical responses across multiple malignancies through activation of the host immune system. However, only a subset of patients experiences sustained therapeutic benefit because of the remarkable complexity and heterogeneity of tumor–immune interactions. Accurate prediction of immunotherapy response remains one of the greatest challenges in precision oncology. Recent advances in foundation artificial intelligence (AI) models have introduced a new paradigm for predictive immuno-oncology by enabling large-scale multimodal representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, immunomics, spatial biology, laboratory biomarkers, electronic health records, and longitudinal clinical data. Unlike conventional task-specific machine learning algorithms, foundation models learn generalized biomedical representations through self-supervised pretraining that can be adapted across numerous downstream oncology applications. Transformer architectures, multimodal large language models, graph neural networks, retrieval-augmented generation, generative AI, and multimodal foundation models have demonstrated remarkable potential for predicting immunotherapy response, characterizing the tumor microenvironment, identifying novel biomarkers, optimizing treatment selection, and supporting adaptive clinical decision-making. These intelligent systems further facilitate integration of radiomics, computational pathology, spatial multi-omics, digital twins, and real-world clinical evidence into comprehensive precision immunotherapy workflows. Despite substantial progress, important challenges remain regarding data harmonization, explainability, computational scalability, interoperability, cybersecurity, regulatory validation, and equitable clinical implementation. This review provides a comprehensive overview of foundation AI models in predictive immunooncology, highlighting computational principles, current clinical applications, implementation challenges, and future perspectives for personalized immunotherapy.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/78 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 53-61 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/78/68 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/79 2026-08-06T16:17:02Z files:ART

Computational Tumor Microenvironment Modeling Using Artificial Intelligence and Digital Twins Dr.Mehul Shah Dr.Pooja Menon Dr.Akshay Kulkarni Mrs.Lakshmi Rao Dr.Imran Khan Dr.Sneha Dutta Mr.Vivek Sinha Tumor microenvironment, Artificial intelligence, Digital twins, Precision oncology, Computational oncology, Immunomics, Systems biology, Digital pathology, Multimodal learning, Personalized medicine. The tumor microenvironment (TME) is increasingly recognized as a critical determinant of cancer initiation, progression, metastasis, therapeutic response, and clinical outcomes. Rather than functioning as isolated populations of malignant cells, tumors exist within dynamic ecosystems composed of immune cells, stromal fibroblasts, endothelial cells, extracellular matrix components, cytokines, metabolites, vascular networks, and diverse signaling pathways that collectively influence disease evolution. Recent advances in artificial intelligence (AI), systems biology, computational oncology, digital twins, spatial multiomics, computational pathology, and multimodal biomedical data integration have enabled comprehensive computational modeling of the tumor microenvironment. AI-driven digital twins create continuously evolving virtual representations of individual tumors by integrating radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, immunomics, laboratory biomarkers, wearable technologies, and longitudinal clinical information to simulate tumor–host interactions and therapeutic responses. Machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, multimodal foundation models, and generative AI facilitate characterization of cellular communication, immune dynamics, stromal remodeling, angiogenesis, and spatial biological organization while supporting personalized diagnosis, prognostic prediction, immunotherapy optimization, adaptive therapeutics, and clinical decision support. Despite remarkable technological progress, challenges remain regarding data harmonization, computational complexity, explainability, interoperability, cybersecurity, regulatory validation, and ethical implementation. This review provides a comprehensive overview of computational tumor microenvironment modeling using artificial intelligence and digital twins, highlighting computational foundations, clinical applications, emerging innovations, and future perspectives for precision oncology.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/79 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 62-70 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/79/69 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/80 2026-08-06T16:28:19Z files:ART

Pan-Cancer Multimodal Intelligence: Integrating Radiomics, Pathomics, Genomics, and Electronic Health Records Dr.Kavitha Nair Dr.Rohit Agarwal Dr.Simran Kaur Pan-cancer, Artificial intelligence, Radiomics, Pathomics, Genomics, Electronic health records, Multimodal learning, Precision oncology, Foundation models, Clinical decision support. Cancer represents a heterogeneous group of diseases characterized by diverse molecular alterations, complex tumor microenvironments, variable therapeutic responses, and distinct clinical outcomes across different organ systems. Although advances in molecular biology, targeted therapeutics, immunotherapy, and precision medicine have significantly improved cancer management, effective individualized care increasingly depends upon comprehensive integration of heterogeneous biomedical information rather than isolated diagnostic modalities. Recent developments in artificial intelligence (AI) have introduced the concept of pan-cancer multimodal intelligence, which integrates radiomics, digital pathomics, genomics, transcriptomics, proteomics, laboratory biomarkers, electronic health records (EHRs), and longitudinal clinical data into unified computational frameworks capable of supporting precision oncology across multiple cancer types. Machine learning, deep learning, transformer architectures, graph neural networks, multimodal foundation models, self-supervised learning, retrievalaugmented generation, and generative AI have substantially enhanced multimodal biomedical representation learning while improving diagnosis, prognostic prediction, molecular characterization, therapeutic optimization, immunotherapy response prediction, digital twins, and intelligent clinical decision support. Despite remarkable technological progress, important challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, regulatory validation, and equitable implementation. This review provides a comprehensive overview of pancancer multimodal intelligence, emphasizing computational principles, clinical applications, implementation challenges, and future perspectives for precision cancer medicine.[1 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/80 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 71-79 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/80/71 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/81 2026-08-07T02:35:09Z files:ART

Generative Artificial Intelligence in Oncology: From Clinical Documentation to Precision Therapeutic Discovery Dr.Chetan Deshpande Dr. Asha Fernandes Dr. Vishal Reddy Mrs. Priyanka Bose Dr. Nitin Goyal Generative artificial intelligence, Oncology, Large language models, Precision medicine, Drug discovery, Clinical documentation, Digital pathology, Foundation models, Computational oncology, Personalized therapeutics. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The rapid growth of biomedical data generated from radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory investigations, wearable technologies, and electronic health records has created unprecedented opportunities for artificial intelligence (AI)-driven oncology. Among recent innovations, generative artificial intelligence (GenAI) has emerged as a transformative technology capable of generating text, images, molecular structures, synthetic biomedical data, computational models, and clinical knowledge that support nearly every stage of cancer care. Unlike conventional discriminative AI models that primarily classify or predict outcomes, generative AI synthesizes new information by learning complex probability distributions from large multimodal datasets. Advances in transformer architectures, large language models (LLMs), diffusion models, multimodal foundation models, retrieval-augmented generation (RAG), graph neural networks, reinforcement learning, and agentic AI have significantly expanded the role of GenAI in oncology. These intelligent systems are increasingly applied to clinical documentation, medical imaging, digital pathology, biomarker discovery, molecular design, drug development, clinical trial optimization, patient communication, clinical decision support, and precision therapeutics. Despite remarkable progress, important challenges remain regarding hallucination, explainability, regulatory validation, cybersecurity, ethical governance, data privacy, algorithmic bias, and responsible clinical implementation. This review provides a comprehensive overview of generative artificial intelligence in oncology, emphasizing computational foundations, current clinical applications, implementation challenges, and future perspectives for precision cancer medicine.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/81 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 80-88 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/81/72 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/82 2026-08-07T02:39:51Z files:ART

Self-Supervised Learning for Multimodal Precision Oncology: Unlocking Unlabeled Clinical and Imaging Data Dr.Arpita Sen Dr.Harsh Vardhan Dr.Reshma Ali Dr.Gokul Krishnan Self-supervised learning, Precision oncology, Artificial intelligence, Multimodal learning, Medical imaging, Digital pathology, Foundation models, Computational oncology, Multi-omics, Personalized medicine. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. Modern oncology continuously generates enormous quantities of heterogeneous biomedical data through radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory investigations, electronic health records, wearable technologies, and longitudinal clinical documentation. Although these datasets possess tremendous potential for advancing precision oncology, only a small proportion is manually annotated, creating a major limitation for conventional supervised artificial intelligence (AI). Self-supervised learning (SSL) has emerged as a transformative machine learning paradigm that enables models to learn generalized biomedical representations directly from unlabeled data without requiring extensive manual annotation. By leveraging pretext tasks, contrastive learning, masked modeling, multimodal representation learning, transformer architectures, graph neural networks, and foundation models, SSL has significantly improved computational oncology across medical imaging, digital pathology, radiogenomics, computational pathology, multi-omics integration, clinical decision support, and personalized therapeutics. Furthermore, SSL facilitates transfer learning, few-shot learning, multimodal foundation models, digital twins, and agentic AI, thereby accelerating development of generalized biomedical intelligence for precision medicine. Despite remarkable advances, important challenges remain regarding multimodal data harmonization, computational scalability, interpretability, regulatory validation, and clinical translation. This review provides a comprehensive overview of self-supervised learning in multimodal precision oncology, highlighting computational foundations, current applications, implementation challenges, and future perspectives for unlocking the full potential of unlabeled biomedical data.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/82 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 89-97 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/82/73 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/83 2026-08-07T02:45:47Z files:ART

Artificial Intelligence for Computational Theranostics: Integrating Molecular Imaging, Nanomedicine, and Precision Oncology Dr.Nishita Roy Dr.Arun Mathew Dr.Megha Sharma Mrs.Aditi Rao Dr.Salman Ahmed Dr.Jyoti Verma Computational theranostics, Artificial intelligence, Molecular imaging, Nanomedicine, Precision oncology, Radiomics, Machine learning, Precision medicine, Digital twins, Clinical decision support. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The convergence of molecular imaging, nanomedicine, and artificial intelligence (AI) has given rise to computational theranostics, an emerging paradigm that combines diagnosis, therapy, therapeutic monitoring, and personalized decision-making within unified computational frameworks. Molecular imaging modalities—including positron emission tomography (PET), single-photon emission computed tomography (SPECT), computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and optical imaging—provide noninvasive visualization of tumor biology, while nanomedicine enables targeted drug delivery, molecular imaging enhancement, and precision therapeutics. Artificial intelligence, through machine learning, deep learning, multimodal learning, transformer architectures, graph neural networks, generative AI, and foundation models, integrates these diverse biomedical datasets with genomics, digital pathology, laboratory biomarkers, and longitudinal clinical information to optimize diagnosis, treatment selection, therapeutic response prediction, and personalized cancer care. AI-driven computational theranostics has demonstrated remarkable potential in radiomics, radiopharmaceutical development, nanoparticle engineering, immunotherapy prediction, adaptive treatment planning, digital twins, and clinical decision support. Despite substantial progress, important challenges remain regarding multimodal data integration, explainability, computational scalability, regulatory validation, cybersecurity, and ethical implementation. This review provides a comprehensive overview of computational theranostics, highlighting current innovations, clinical applications, implementation challenges, and future perspectives for precision oncology.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/83 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 98-106 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/83/74 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/84 2026-08-07T02:59:29Z files:ART

Next-Generation Cancer Intelligence Platforms: Converging Foundation Models, Digital Twins, and Precision Medicine Dr. Kiran Patel Dr. Nandita Singh Dr. Faisal Qureshi Mrs. Reema Das Dr. Prakash Joshi Cancer intelligence platforms, Foundation models, Digital twins, Precision oncology, Artificial intelligence, Computational oncology, Personalized medicine, Multimodal learning, Clinical decision support, Systems biology. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, targeted therapeutics, immunotherapy, and precision medicine. The rapid expansion of biomedical data generated from radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, and electronic health records has created unprecedented opportunities for computational oncology while simultaneously presenting substantial challenges for clinical interpretation. Nextgeneration cancer intelligence platforms have emerged as an integrated computational paradigm that combines foundation models, digital twin technology, multimodal artificial intelligence (AI), systems biology, and precision medicine into continuously learning clinical ecosystems. Unlike conventional decision-support systems that analyze isolated datasets, these intelligent platforms integrate multimodal biomedical information to generate adaptive patient-specific models capable of supporting diagnosis, molecular characterization, therapeutic optimization, toxicity prediction, digital simulation, clinical trial matching, and longitudinal disease monitoring. Advances in transformer architectures, multimodal foundation models, graph neural networks, large language models, agentic AI, reinforcement learning, generative AI, federated learning, and cloudnative computing have accelerated development of intelligent oncology ecosystems capable of real-time clinical reasoning and personalized therapeutic decision-making. Despite substantial progress, challenges remain regarding interoperability, computational scalability, explainability, cybersecurity, ethical governance, regulatory validation, and equitable clinical implementation. This review provides a comprehensive overview of next-generation cancer intelligence platforms, highlighting computational principles, clinical applications, implementation challenges, and future perspectives for the convergence of foundation models, digital twins, and precision medicine. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/84 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 107-115 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/84/75 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/85 2026-08-07T03:06:03Z files:ART

Towards Autonomous Precision Oncology: AI Agents, Digital Twins, and Multimodal Clinical Intelligence for Personalized Cancer Care Dr. Tanmay Kulshreshtha Dr. Padmini Iyer Dr. Rajiv Menon Mrs. Sonal Gupta Akhil Kapoor Dr. Meena Joseph Mr. Arvind Sethi Autonomous precision oncology, AI agents, Digital twins, Multimodal clinical intelligence, Precision medicine, Artificial intelligence, Computational oncology, Foundation models, Clinical decision support, Personalized cancer care. Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The extraordinary biological complexity of malignant diseases, combined with rapidly expanding volumes of radiological imaging, digital pathology, genomic sequencing, multi-omics, laboratory investigations, wearable technologies, and longitudinal clinical data, has created an urgent need for intelligent computational systems capable of supporting personalized oncology. Recent advances in artificial intelligence (AI) have introduced the concept of autonomous precision oncology, where AI agents, digital twins, multimodal foundation models, and real-time clinical intelligence operate together within integrated healthcare ecosystems. Unlike conventional AI systems designed for isolated prediction tasks, autonomous oncology platforms continuously perceive, reason, plan, execute, monitor, and learn from dynamic clinical environments while maintaining physician oversight. AI agents coordinate diagnostic workflows, digital twins simulate disease evolution and therapeutic response, and multimodal clinical intelligence integrates imaging, pathology, multi-omics, electronic health records, and real-world clinical outcomes into comprehensive patient-specific computational representations. Advances in transformer architectures, graph neural networks, reinforcement learning, retrievalaugmented generation, agentic AI, federated learning, and generative AI have significantly accelerated development of these intelligent oncology ecosystems. Despite remarkable progress, important challenges remain regarding explainability, interoperability, computational scalability, cybersecurity, regulatory validation, ethical governance, clinician acceptance, and equitable implementation. This review presents a comprehensive overview of autonomous precision oncology, highlighting computational principles, clinical applications, implementation challenges, and future perspectives for personalized cancer care driven by intelligent computational ecosystems.[1] International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2025-12-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/85 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 1, Issue 5, December 2025; 116-125 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/85/76 Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/87 2026-09-14T06:58:32Z files:ART

Epidemiology, Host Specificity and Integrated Control of Postharvest Green Mold Disease Caused by Penicillium digitatum on Citrus Fruits Eman M. Gebreil Nagwa S.A.ALraaydi Penicillium digitatum, Citrus Fruits, Spore Dispersal, Fruit Infection, Disease Management. In study aerodynamic spore mechanism and epidemology of Penicillium digitatum on citrus fruits stadied : Orange varities ( Blood , Sweet, Sour and Navel ) , mandarine , lemon and grapefruits the results showed the spores of Penicillium digitatum have low weight to became longtime before fall down , indicating that Penicillium digitatum have dry spores . The sedimentation of these spores on fruit surfaces was more vertically faster than horizontally, conferming the way of storage fruits above long distance from ground surface does not prevent the infection incidence . From studying of lignification effect on peels of studied citrus varities after injuires and kept for 12 hours then injection showed the lignin formation do not prevent the infection but just lated about 6 days . From study specific different isolates showed all species have specific to infect the host fruit without the other . Through study the maturity of lemon fruits on enhance or lated infection with Penicillium digitatum show the mature lemon fruits infected earlier through 4 days compared with immature lemon fruits infected after 6 days. Finally the results optained from programs to this disease on injected and non-injected fruits by selective two physical methods ( Hot water – Sea water ) , two natural oils (Eucalyptus oil – Olive oil) , one mineral oil ( Johnson's baby oil ) and wax , showed all of these method can not prevent the infection but just delated the infection period , wax test delay five days and the other two treatments were delay about three days only . International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-08-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/87 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 8, August 2026; 1-10 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/87/77 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/88 2026-09-08T12:08:34Z files:ART

Interplay of Glucose, Vitamin C, and Adiponectin in Gestational Diabetes Mellitus: Insights from Kaduna State, Nigeria Sarah Nuhu Kase Friday K. Iweka Amaechi Dennis Christy Chinyere Fredrick Nasiru Lawal Jamilah Suleiman Garba Ninani Iyanuoluwa Ifeoluwa Ojo Nwokorie Kelechukwu Sylvester Gestational diabetes mellitus, adiponectin, vitamin C, glucose, oxidative stress, Nigeria Background: Gestational diabetes mellitus (GDM) is a pregnancy-related metabolic disorder characterized by glucose intolerance and insulin resistance, usually diagnosed during the second or third trimester. It increases the risk of adverse maternal and fetal outcomes if not properly managed. This study evaluated glucose, vitamin C, and adiponectin levels among women with GDM in Kaduna State, Nigeria. Methods: A case-control study involving 160 women aged 18–40 years was conducted, comprising 60 women with GDM, 50 healthy pregnant women, and 50 healthy non-pregnant women. Participants were recruited from selected hospitals in Kaduna State. Sociodemographic and clinical data were collected using a structured questionnaire. GDM was diagnosed using a 50 g oral glucose challenge test followed by a 75 g oral glucose tolerance test. Venous blood samples were collected for biochemical analysis. Data were analyzed using appropriate statistical methods, with significance set at p < 0.05. Results: Women with GDM had significantly lower adiponectin levels than both control groups (p < 0.05). During the oral glucose challenge test, adiponectin levels were highest in healthy pregnant women (10.13 ± 0.28), followed by healthy non-pregnant women (6.51 ± 0.24), and lowest in women with GDM (4.33 ± 0.22). Similar trends were observed following the oral glucose tolerance test. Conclusion: Reduced adiponectin levels were strongly associated with GDM, suggesting that adiponectin may serve as a potential biomarker for early detection and improved metabolic monitoring of women at risk of gestational diabetes in Nigeria. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-09-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/88 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 9, September 2026; 1-11 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/88/78 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/89 2026-09-15T10:00:35Z files:ART

Examining the influence of electromagnetic fields on wheat (Triticum aestivum L.) plants under water stress (drought) Aiad Abdelkareim Akhreim Alzway Waad Farj Mohammed Magnetic fields (MF), Wheat (Triticum aestivum), Drought stress, Seed treatment, Growth parameters, Protein analysis. Magnetic fields have garnered significant interest from the scientific community due to their potential to improve seed germination, seedling growth, and various physiological, biochemical, and yield-related traits in plants, including cereal crops and vegetables. Over the years, research has highlighted the magnetic field (MF) as a safe and effective alternative for enhancing agricultural productivity. This study aimed to evaluate the impact of magnetic field pre-treatment on wheat (Triticum aestivum), a major crop in Libya and globally, primarily used for human nutrition due to its high content of proteins, minerals, and carbohydrates. One of the most important applications of magnetic fields is the treatment of wet seeds. The study also explored the effects of magnetic fields applied at different durations (control, one day, two days). Growth parameters such as plant height, number of leaves per plant, root length, fresh weight, and dry matter were assessed, along with yield characteristics like number of seeds per spike, 100-grain weight, and total grain yield under drought stress. Additionally, physiological aspects such as mineral nutrient content, monosaccharides, disaccharides, total soluble sugars, crude protein, proline, auxin levels, and protein analysis using SDS-PAGE were measured. The results showed that magnetic field treatments significantly improved plant growth compared to untreated seeds under drought stress. The findings indicate that pre-sowing magnetic field treatment has the potential to enhance the development of wheat seeds, improving growth parameters, mineral nutrients, total soluble sugars, pigments, and proteins. Notably, exposing seeds to the magnetic field for two days produced the best results. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-09-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/89 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 9, September 2026; 12-20 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/89/79 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0/

oai:ojs.ijmrbpdms.org:article/90 2026-09-29T09:23:44Z files:ART

Protective Effects of Chromolaena odorata on Oxidative Stress Biomarkers and Penile Histoarchitecture in Paroxetine-Induced Erectile Dysfunction Onoja, B. A. Oyewopo, A. O Olowolayemo, K. O. Adewunmi, I. A. Chromolaena odorata, erectile dysfunction, oxidative stress, paroxetine, penile histoarchitecture, malondialdehyde, glutathione, superoxide dismutase, catalase, Wistar rats, SSRI, neuroprotection. Paroxetine is the most serotonergically active SSRI, and is linked to the most severe degree of sexual dysfunction, such as erectile dysfunction (ED) occurring in 30–70% of male patients. It works through inhibition of neuronal and endothelial nitric oxide synthase (nNOS/eNOS), inhibition of NO-cGMP signalling pathway, serotonin-dopamine antagonism, hormonal dysregulations and inducing oxidative stress. Chromolaena odorata (L.) The medicinal plant R.M. King & H. Robinson (Asteraceae) has been well documented with antioxidants, anti-inflammatory, vasorelaxant and cytoprotective properties due to its flavonoids and phenolic acids. The protective effects against paroxetine-induced penile oxidative stress and histoarchitectural injury have not been studied. The protective effect of ethanolic leaf extract of C. odorata on oxidative stress biomarkers malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT) and reduced glutathione (GSH) and penile histoarchitecture was therefore evaluated in an ED model caused by paroxetine in adult male Wistar rats. Twenty rats (180–220 g) were randomly divided into four groups (n = 4), namely: Control (distilled water), Paroxetine only (10 mg/kg), Paroxetine + low-dose of C. odorata (100 mg/kg), and Paroxetine + high-dose of C. odorata (200 mg/kg). All the treatments were given for 28 days orally continuously. Paroxetine administration reduced SOD activity (0.0757 ± 0.0048 U/mg vs. 0.1011 ± 0.0106 U/mg in controls) and CAT activity (1.433 ± 0.0923 vs. 2.331 ± 0.4225 U/mg), and elevated MDA levels (0.7830 ± 0.0380 µM vs. 0.6483 ± 0.0041 µM). The co-administration of low doses of C. odorata significantly increased GSH (0.6343 ± 0.0657 mM) when compared to all the other groups (p < 0.05). The highest was observed in the low dose group (1.139 ± 0.2090 µM), potentially due to pro-oxidant activity of polyphenolic compounds with concentration. Cavernosal disorganisation, eosinophilic fibrosis, and compression of the sinusoidal spaces in penile tissue were the most significant histopathologic findings in penile tissue following paroxetine treatment, whereas neuropil vacuolation and pyknotic neuronal nuclei were the most significant neuropathologic findings in brain tissue. Both doses of C. odorata extract reduced these alterations, with the highest dose showing most complete structural restoration, in the form of well-defined deeply folded mucosal architecture, relaxed and organised smooth muscle stroma, and consolidated neuropil with good neuronal morphology. The present findings suggest a dose-dependent cytoprotective activity against paroxetine-induced penile and neural tissue injury of C. odorata which underscores its potential further investigation as a candidate herbal therapeutic agent for SSRI-associated erectile dysfunction, mainly at the histological level. International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences 2026-10-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijmrbpdms.org/index.php/files/article/view/90 International Journal of Multidisciplinary Research in Biotechnology, Pharmacy, Dental and Medical Sciences ; IJMRBPDMS:Vol 2, Issue 10, October 2026; 1-10 3108-2971 eng https://ijmrbpdms.org/index.php/files/article/view/90/80 Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by/4.0/