Biomed Thought is a peer-reviewed, open-access journal published by MIND QUEST RESEARCH & EDUCATIONAL CENTER (SMC-PRIVATE) LIMITED. The journal aims to bridge the gap between basic science and clinical applications, providing a platform for innovative research that advances our understanding of human health and disease. By making all articles freely accessible to researchers, clinicians, and healthcare professionals around the world, Biomed Thought ensures that important discoveries reach a global audience without barriers. Scope: Biomed Thought covers a wide range of topics within the biomedical and clinical sciences. Key areas of interest include: Biomedical Research: Investigating the molecular mechanisms of diseases, developing new diagnostics, and advancing treatment strategies. Clinical Medicine: Translating research findings into clinical practice, including clinical trials, new therapies, and patient care improvements. Biotechnology and Bioengineering: Innovations in biotechnology, bioinformatics, medical devices, and computational biology. Regenerative Medicine and Stem Cells: Research on tissue repair, regenerative therapies, and stem cell applications in medicine. Pharmaceuticals and Drug Development: The development of novel therapeutics, drug delivery systems, and vaccine technologies. Genomics and Precision Medicine: Advances in genomic research, personalized medicine, and the use of big data in healthcare. Public Health and Epidemiology: Studies on public health challenges, disease prevention, and global health initiatives. Publication Frequency: Semi-Annual
MIND QUEST RESEARCH & EDUCATIONAL CENTER (SMC-PRIVATE) LIMITED
IMPACT OF LIFESTYLE INTERVENTIONS ON TYPE 2 DIABETES MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW
Type 2 diabetes mellitus is one of the most important global health-related threats in the 21 st century, and by 2045, the prevalence rate is expected to reach over 780 million. It has long been argued that lifestyle interventions involving dietary change, physical exercise, and behavior change are the foundational strategies in diabetes management, although concerns have existed over whether they have been compared in their effectiveness, the optimal mode of delivery, and reducing clinical changes in the long term. The systematic review, which was carried out according to PRISMA 2020 standards, was a synthesis of evidence based on the randomized controlled trial, published between 2019 and 2025, to assess the effects of lifestyle interventions on glycemic control and other metabolism-related outcomes in people with type 2 diabetes. A thorough search of databases at Scopus, Web of Science, PubMed, and Cochrane Library found 1,847 records, and 42 articles that were included after full text screening. Thematic synthesis resulted in four pervasive paradigms namely technology mediated interventions, dietary modification programmes, physical activity programmes and theory-based behavioral programmes. Results have shown that lifestyle interventions, on a consistent basis, lead to better glycemic control, and meta-analytic evidences indicate that the glycated hemoglobin levels decrease by 0.07 up to 0.73 percent according to the intensity and duration of intervention. There is, however, a high level of heterogeneity between studies and the sustainability of treatment effects after twelve months has not been well described. Interventions provided by telemedicine have the same effectiveness as face-to-face programs in terms of loss, whereas low-carbohydrate dietary methods have a specific potential in the short-term glycemic control. Interventions based on theories that target readiness to behavioral change have shown clinically significant changes in HbA1c levels, but also have methodological weaknesses that undermine the confidence of effect estimates. This literature review finds that lifestyle interventions constitute part of effective interventions in diabetes management, but they are most effective with continuous behavioral support and individual model that takes into concern the patient preferences, duration of the disease and comorbidities.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE IN EARLY DIAGNOSIS OF CARDIOVASCULAR DISEASES: A SYSTEMATIC LITERATURE REVIEW
Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, necessitating innovative approaches for early detection and risk stratification. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a transformative tool in cardiovascular medicine, offering enhanced diagnostic accuracy and predictive performance. This systematic review aims to synthesize contemporary evidence on the application of AI technologies in the early diagnosis and risk prediction of cardiovascular diseases, highlighting methodological trends, clinical performance, and emerging innovations. The review was conducted in accordance with PRISMA guidelines. A comprehensive literature search was performed across PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for studies published between 2019 and 2025. Studies evaluating AI-based diagnostic or predictive models for cardiovascular conditions—including coronary artery disease, heart failure, arrhythmias, and multimodal risk prediction—were included. Data extraction encompassed study characteristics, AI methodologies, data modalities, validation strategies, and performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Methodological quality was assessed using standardized appraisal tools. A total of 85 studies were included in the final synthesis. Deep learning approaches, particularly convolutional neural networks for imaging and electrocardiogram analysis, demonstrated superior performance across multiple applications. Reported accuracy ranged from 82% to 97%, with AUC values frequently exceeding 0.90, especially in arrhythmia detection and coronary artery disease prediction. Multimodal AI models integrating electronic health records, imaging, and biomarker data achieved enhanced predictive capability compared to traditional risk scores. External validation was reported in 61% of studies, supporting generalizability across diverse populations.AI-driven diagnostic and predictive models consistently outperform conventional methods in cardiovascular disease detection and risk stratification. While significant progress has been achieved, challenges related to interpretability, external validation, clinical integration, and ethical implementation remain critical considerations. Continued advancement in explainable AI and multimodal data fusion is essential to translate these technologies into routine cardiovascular care.
AI-POWERED DIETARY RECOMMENDATION SYSTEMS FOR OPTIMIZED GLYCEMIC CONTROL IN TYPE 2 DIABETES
Effective dietary management is essential for maintaining glycemic stability in individuals with Type 2 Diabetes, yet traditional nutritional guidelines often overlook the substantial inter-individual variability in postprandial glucose responses. This study presents an AI-powered dietary recommendation system that integrates continuous glucose monitoring data, nutritional composition features, and personalized metabolic patterns to predict glycemic excursions and generate optimized meal recommendations. Using a hybrid deep-learning architecture enhanced with temporal modeling and attention-based interpretability, the system accurately modeled nonlinear nutrient–glucose interactions and produced individualized predictions that outperformed conventional glycemic estimation approaches. A multi-objective optimization engine further refined dietary recommendations by balancing predicted glycemic impact with nutritional adequacy and patient-specific dietary constraints. Experimental results demonstrated strong predictive performance, stable confidence across diverse meal categories, and clinically meaningful nutrient–glycemic associations. Expert dietitians validated the interpretability and suitability of the recommendations, ensuring alignment with established clinical nutrition standards. While limitations include variability in CGM accuracy and incomplete meal-logging adherence, the findings provide compelling evidence that AI-driven dietary systems can significantly improve glycemic control strategies. This research highlights the promise of precision nutrition technologies and establishes a scalable, clinically relevant framework for integrating AI-based dietary recommendations into diabetes-care ecosystems.
Copyright (c) 2025 Younas Rehman, Muhammad Inam Farooq (Author) https://creativecommons.org/licenses/by/4.0
LEVERAGING AI-BASED PREDICTIVE SYSTEMS FOR REAL-TIME EPIDEMIC SURVEILLANCE AND CONTROL
Real-time epidemic surveillance is essential for rapid detection, early intervention, and effective outbreak control, yet traditional surveillance systems struggle to capture rapidly evolving transmission dynamics. This study proposes an AI-based predictive framework designed to enhance epidemic monitoring by integrating multimodal data streams, including regional case trajectories, mobility patterns, symptom-reporting trends, and spatiotemporal risk indicators. The system employs a hybrid deep-learning architecture incorporating temporal modeling, nonlinear feature extraction, and attention-driven interpretability to forecast outbreak trajectories and detect potential hotspots. Experimental results demonstrate that the model consistently outperforms conventional forecasting approaches in capturing early transmission acceleration, identifying emerging high-risk regions, and generating stable probability-based outbreak predictions. Visualization of learned spatiotemporal patterns highlights the system’s ability to uncover hidden epidemic structures that traditional methods often fail to detect. Epidemiological expert review further confirmed that the model prioritizes meaningful predictors aligned with known transmission pathways and behavioral changes. While limitations exist due to data incompleteness, noise, and regional reporting variability, the findings provide strong evidence that AI-driven predictive systems can significantly improve real-time epidemic intelligence, enabling faster response, better allocation of public-health resources, and more accurate early-warning capabilities. This study contributes a scalable and interpretable AI framework for modern epidemic surveillance, laying the foundation for precision-driven outbreak prediction and proactive health-system preparedness.
Copyright (c) 2025 Roohan Ahmad, Syeda Iram Batool, Muhammad Rehan (Author) https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING APPROACHES FOR EARLY DETECTION OF COGNITIVE DECLINE IN NEUROPSYCHIATRIC DISORDERS
Early detection of cognitive decline in neuropsychiatric disorders remains a critical challenge, as traditional clinical assessments often fail to capture subtle impairments that emerge during the earliest phases of disease progression. This study proposes a multimodal machine-learning framework designed to identify early cognitive deterioration by analyzing neurocognitive performance markers, behavioral response-time variability, linguistic and speech-derived features, and neurophysiological signal complexity. After rigorous preprocessing and feature extraction, a set of supervised learning models was trained to distinguish cognitively stable individuals from those exhibiting early signs of decline. The system demonstrated strong predictive performance, with latent-feature clustering and attention-based interpretability indicating that the models successfully captured nonlinear and multidimensional patterns associated with early impairment. Expert clinical review further validated the meaningful alignment between model-identified features and known cognitive biomarkers linked to executive dysfunction, memory instability, and semantic degradation. Although limitations exist related to dataset size and recording variability, the results provide compelling evidence that machine-learning approaches can significantly improve proactive detection, risk stratification, and monitoring of cognitive decline in neuropsychiatric populations. These findings highlight the potential of AI-driven cognitive analytics to support precision mental-health care and enable earlier, more targeted interventions.
Copyright (c) 2025 Abdul Ghaffar, Rabia Kiran (Author) https://creativecommons.org/licenses/by/4.0
PREDICTIVE MODELING OF CARDIOVASCULAR EVENTS USING AI-ENHANCED ECHOCARDIOGRAPHIC FEATURE EXTRACTION
Accurate prediction of major cardiovascular events remains a critical priority in preventive cardiology, yet traditional echocardiographic interpretation is limited by operator subjectivity and insufficient sensitivity to subtle myocardial abnormalities. This study presents an AI-enhanced predictive modeling framework that leverages advanced echocardiographic feature extraction to improve early risk stratification. Using a hybrid deep-learning architecture integrating convolutional spatial encoders and temporal attention mechanisms, the system automatically quantified structural, functional, and deformation-based cardiac parameters from standard echocardiographic videos. A dataset of labeled clinical outcomes enabled supervised model training, with performance evaluated across multiple metrics including accuracy, sensitivity, specificity, and predictive probability distribution. Results demonstrated that AI-derived strain patterns, wall-motion trajectories, and cycle-based temporal signatures significantly improved predictive performance compared to conventional echocardiographic assessments. Qualitative expert review additionally confirmed strong physiologic relevance in the model’s attention maps, supporting clinical interpretability. The findings suggest that AI-driven echocardiographic analytics can serve as a powerful prognostic tool, enhancing early detection of high-risk individuals and advancing the field toward precision cardiovascular care. Further large-scale validation is recommended to support integration into routine clinical workflows.
Copyright (c) 2025 Zafar Aleem Sucha (Author) https://creativecommons.org/licenses/by/4.0
INTEGRATING DEEP NEURAL NETWORKS FOR RADIO PATHOLOGICAL CORRELATION IN EARLY CANCER DIAGNOSTICS: A MULTI-MODAL ANALYSIS
Early cancer detection remains a central challenge in clinical oncology, largely due to the limitations of unimodal diagnostic approaches that consider radiological and histopathological data independently. This study presents a comprehensive multi-modal deep-learning framework designed to integrate radiology and pathology into a unified diagnostic system capable of identifying early-stage malignancies with higher accuracy and interpretability. Using paired CT, MRI, mammography, and whole-slide histopathology images, the model employs dual encoders and an attention-based fusion mechanism to learn cross-modal associations that reflect both macroscopic structural abnormalities and microscopic cellular transformations. Quantitative experiments demonstrate substantial improvements in classification performance, including notable gains in sensitivity, specificity, and prediction confidence compared with single-modality baselines. Qualitative assessments by radiologists and pathologists further validate that the system’s attention maps consistently highlight clinically meaningful regions, indicating strong alignment with established radiopathological reasoning. The model also showed robust generalizability across multiple cancer types, reinforcing the potential of multi-modal fusion to reduce diagnostic ambiguity and enhance early detection reliability. While challenges related to dataset heterogeneity and institutional variability persist, the findings confirm that radiopathological integration provides a highly promising pathway toward building advanced, clinically deployable AI-assisted screening tools. Overall, this research establishes multi-modal deep learning as a powerful strategy for bridging imaging and tissue-level diagnosis, ultimately supporting more timely and accurate clinical decision-making in early cancer diagnostics.
Copyright (c) 2025 Hassan Yar Mahsood (Author) https://creativecommons.org/licenses/by/4.0
MRI-BASED ASSESSMENT OF WHITE MATTER INTEGRITY IN PEDIATRIC TRAUMATIC BRAIN INJURY
Pediatric traumatic brain injury (TBI) poses significant diagnostic and prognostic challenges due to the complex and often subtle nature of white matter damage in the developing brain. This study employed advanced magnetic resonance imaging (MRI) modalities—including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), magnetic resonance spectroscopy (MRS), and volumetric MRI—to assess white matter integrity and its relationship with cognitive outcomes in children with TBI. Quantitative analyses revealed decreased fractional anisotropy (FA) and increased mean diffusivity (MD) across key white matter regions such as the corpus callosum, cingulum, and internal capsule, indicating disrupted axonal integrity. DKI and NODDI metrics further supported these findings, with reduced kurtosis values and neurite density in frontal and temporal white matter. MRS data showed significant reductions in N-acetylaspartate (NAA) and elevations in choline, reflecting neuronal loss and membrane turnover, respectively. Volumetric MRI indicated notable brain atrophy, particularly in the hippocampus and frontal lobe, with volume losses exceeding 10% in several cases. Correlation analysis demonstrated strong associations between imaging biomarkers (e.g., FA, neurite density) and cognitive performance scores (r > 0.5, p < 0.01), highlighting the prognostic value of neuroimaging in pediatric TBI. These findings validate the use of multimodal MRI as a sensitive, non-invasive tool for detecting white matter abnormalities and predicting functional outcomes. The study supports the integration of advanced imaging into clinical protocols for early diagnosis, monitoring, and personalized rehabilitation planning in pediatric TBI. The robust imaging biomarkers identified herein pave the way for precision medicine approaches, offering new avenues for improving outcomes and long-term quality of life in this vulnerable population.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
USE OF METFORMIN IN NON-DIABETIC PATIENTS WITH POLYCYSTIC OVARY SYNDROME: HORMONAL EFFECTS
This prospective, single‐arm study investigated the endocrine effects of metformin in 50 non‐diabetic women with polycystic ovary syndrome (PCOS) over 12 weeks. Participants (age 18–35 years; BMI 18.5–29.9 kg/m²) meeting Rotterdam criteria and exhibiting normal glucose tolerance received metformin 500 mg three times daily, with adherence assessed by pill counts. Fasting blood samples collected at baseline and study end quantified total testosterone, free androgen index (FAI), sex hormone–binding globulin (SHBG), luteinizing hormone (LH), follicle‐stimulating hormone (FSH), insulin, and glucose; insulin resistance was estimated via HOMA‐IR. Metformin treatment yielded a 30.2% reduction in total testosterone (61.2 ± 9.8 to 42.7 ± 7.1 ng/dL; p < 0.001) and a 43.1% decrease in FAI (5.1 ± 1.0 to 2.9 ± 0.7; p < 0.001), accompanied by a 38.3% rise in SHBG (40.7 ± 4.9 to 56.3 ± 6.2 nmol/L; p < 0.001). LH declined by 14.8% (8.1 ± 1.4 to 6.9 ± 1.1 IU/L; p < 0.001) while FSH increased by 10.2% (4.9 ± 0.9 to 5.4 ± 0.8 IU/L; p = 0.005). HOMA‐IR improved by 22.2% (2.7 ± 0.6 to 2.1 ± 0.5; p < 0.001), and reductions in HOMA‐IR correlated strongly with decreases in FAI (r = 0.72) and total testosterone (r = 0.68; both p < 0.001). Full adherence was achieved by 80% of participants, and adverse events were mild. These findings demonstrate that metformin safely and effectively ameliorates hyperandrogenism and insulin resistance in non‐diabetic PCOS patients, supporting its role as a primary endocrine modulator across diverse PCOS phenotypes.
THE ROLE OF GENETIC FACTORS IN THE DEVELOPMENT OF AORTIC ANEURYSMS: IMPLICATIONS FOR EARLY SCREENING AND PREVENTION
This study investigates the role of genetic factors in the development of aortic aneurysms (AAs), with a focus on early screening and prevention. By analyzing genetic data from 150 patients diagnosed with thoracic and abdominal aortic aneurysms, the study identifies several genetic variants significantly associated with increased aneurysm risk. Using whole-exome sequencing (WES) and SNP genotyping, a total of 35 genetic variants were identified, including 12 rare mutations and 23 common polymorphisms. Notably, variants in genes such as FBN1, TGFBR2, ELN, SMAD3, and COL3A1 were strongly linked to aortic aneurysm formation. The study further examines the impact of demographic factors, revealing that older male patients with a family history of aneurysms have a heightened genetic risk. Multivariable logistic regression analysis demonstrated that certain genetic variants, such as those in the TGF-β pathway, significantly increase the likelihood of developing aneurysms. The research also compares genetic screening with traditional clinical risk assessment, showing that genetic testing enhances early detection rates by 25%. A predictive model developed from the genetic risk factors demonstrated an AUC of 0.88, indicating high predictive accuracy. This study underscores the potential of integrating genetic screening into clinical practice for early identification and intervention, ultimately improving patient outcomes. The findings provide crucial insights into the genetic underpinnings of aortic aneurysms and highlight the need for personalized, genetics-based prevention strategies.
THE ROLE OF POSTPARTUM DEPRESSION IN MATERNAL-INFANT BONDING: CLINICAL EVALUATION AND SUPPORT STRATEGIES
Postpartum depression (PPD) is a prevalent mental health condition that significantly affects maternal well-being and maternal-infant bonding, with potential long-term consequences for infant development. This study investigates the relationship between PPD severity and maternal-infant bonding, as well as the effectiveness of support strategies in improving bonding outcomes. A mixed-methods approach was employed, incorporating both quantitative clinical assessments, including the Edinburgh Postnatal Depression Scale (EPDS) and the Mother-Infant Bonding Scale (MIBS), and qualitative semi-structured interviews. The results revealed a strong inverse relationship between the severity of postpartum depression and the quality of maternal-infant bonding, with higher depression severity correlating with poorer bonding scores. The study found that mothers with severe depression exhibited significantly lower bonding scores compared to those with mild or moderate depression. In terms of support strategies, cognitive behavioral therapy (CBT) was the most effective intervention, leading to the most significant improvement in maternal-infant bonding. Peer support groups and family therapy also contributed positively to bonding, though to a lesser extent. Additionally, the study highlighted the impact of PPD on infant developmental outcomes, with infants of mothers with severe depression showing delays in emotional regulation and cognitive development. The findings emphasize the need for early detection and intervention for postpartum depression, particularly through integrated mental health screenings in postpartum care. Overall, this study underscores the importance of comprehensive support strategies in mitigating the adverse effects of PPD and fostering healthier maternal-infant relationships. Future research should further explore the long-term effects of these interventions across diverse populations.
ASSOCIATION BETWEEN SLEEP APNEA AND ATRIAL FIBRILLATION RECURRENCE POST-ABLATION
Atrial fibrillation (AF) recurrence after catheter ablation remains a significant clinical concern, and identifying modifiable risk factors is essential for improving outcomes. Sleep apnea has been implicated in cardiac remodeling and arrhythmogenesis, but its role in AF recurrence post-ablation is not well established. This study aimed to investigate the association between sleep apnea and AF recurrence after catheter ablation. In this cohort study, 100 patients who underwent catheter ablation for AF were evaluated for sleep apnea via polysomnography or validated home sleep tests. Baseline demographics, comorbidities, echocardiographic parameters, and thyroid function were recorded. AF recurrence was monitored at 1, 3, 6, and 12 months post-ablation using Holter monitoring. Multivariable Cox regression and Kaplan-Meier analyses were employed to determine predictors of recurrence.Patients with sleep apnea were significantly older, had higher BMI, and a greater burden of comorbidities. Echocardiographic data showed reduced LVEF and enlarged LA volume in the sleep apnea group. AF recurrence at 12 months was observed in 48% of sleep apnea patients versus 22% in controls (p < 0.01). Cox regression confirmed sleep apnea (HR 2.1; 95% CI 1.4–3.2; p = 0.001), elevated TSH, and BMI as independent predictors. Kaplan-Meier analysis demonstrated significantly lower recurrence-free survival in the sleep apnea group. Sleep apnea is a strong and independent predictor of AF recurrence following catheter ablation. Integrating sleep apnea screening and management into AF treatment protocols may enhance long-term outcomes.
DECODING THE GENETIC AND METABOLIC BASIS OF DRUG-INDUCED LIVER INJURY USING IN SILICO SIMULATIONS AND PATIENT-DERIVED HEPATOCYTE ORGANOIDS
Drug-induced liver injury (DILI) is a leading cause of drug withdrawal and acute liver failure, driven by complex interactions between drug compounds and patient-specific genetic and metabolic factors. This study presents an integrated approach combining in silico molecular docking with in vitro patient-derived hepatocyte organoids to evaluate compound toxicity profiles. A panel of 180 compounds was computationally screened for binding affinity against hepatic enzymes, and high-risk candidates were tested on organoid cultures. Results demonstrated that compounds with strong binding affinity (ΔG < −10 kcal/mol) were associated with significantly reduced cell viability and increased markers of cytotoxicity, including LDH release and caspase-3/7 activation. Importantly, patient-derived organoids revealed genotype-dependent differences in toxicity response, underscoring the need for personalized toxicology. Multivariate analysis and clustering further distinguished three hepatotoxicity phenotypes, revealing that compound toxicity cannot be solely predicted by binding affinity. A series of 12 complex visualizations confirmed these trends and highlighted the interdependencies among toxicity indicators. This study establishes a novel hybrid model that leverages artificial intelligence, molecular simulation, and organoid-based experimentation to enhance the predictive accuracy of DILI assessment. The integration of these techniques provides a scalable, ethical, and clinically relevant alternative to traditional toxicity models and holds promise for streamlining the drug development pipeline.
Copyright (c) 2024 Jawad Ali, Muhammad Rehan (Author) https://creativecommons.org/licenses/by/4.0
POPULATION-WIDE RISK STRATIFICATION OF CARDIOVASCULAR EVENTS USING AI-AUGMENTED ECG, EPIGENOMIC BIOMARKERS, AND REAL-WORLD EHR DATA
Cardiovascular diseases remain the leading global cause of death, underscoring the urgent need for accurate, personalized risk stratification tools. This study presents an AI-driven predictive framework that integrates electrocardiogram (ECG) data, epigenomic biomarkers, and electronic health records (EHRs) to enhance cardiovascular risk assessment across diverse populations. A mixed-methods experimental design was employed, incorporating wavelet-transformed ECG signals, normalized CpG epigenomic profiles, structured EHR variables, and NLP-extracted unstructured notes. The fused dataset was modeled using Random Forests, Gradient Boosting, and Artificial Neural Networks, evaluated through 5-fold cross-validation. Performance metrics demonstrated high predictive accuracy with AUCs exceeding 0.90 and improved F1-scores across risk categories. Feature attribution techniques such as SHAP and LIME ensured model interpretability, while decision support outputs provided personalized intervention suggestions. The results, supported by 9 detailed tables and 12 complex visualizations, revealed significant improvements in risk prediction, especially in early-stage detection. This integrated AI methodology not only improves clinical precision but also offers scalable, real-time insights for population health strategies. The study concludes that AI-powered multimodal integration holds substantial promise for transforming cardiovascular diagnostics and preventive care through personalized, data-driven interventions.
Copyright (c) 2024 Dr. Humayun, Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0
A SYSTEMS BIOLOGY APPROACH TO MODELING IMMUNE DYSREGULATION IN LONG COVID PATIENTS: INTEGRATING SINGLE-CELL TRANSCRIPTOMICS, CYTOKINE NETWORKS, AND CLINICAL TRAJECTORIES
COVID is a complex, multisystem condition with persistent symptoms following SARS-CoV-2 infection. This study aimed to characterize its biological, cognitive, and psychosocial dimensions using a mixed-methods experimental design. A cohort of 320 post-COVID-19 individuals was assessed through quantitative biomarker analysis (CRP, IL-6, TNF-α), cognitive testing (MoCA), and symptom inventories. Semi-structured interviews (n = 60) provided qualitative data analyzed via thematic coding. Statistical models including logistic regression and Cox proportional hazards were used to evaluate risk factors. Data triangulation enabled integrative interpretation. Elevated inflammatory markers (mean IL-6 = 6.2 pg/mL; CRP = 4.1 mg/L) were significantly associated with fatigue and dyspnea severity (p < 0.01). Cognitive impairment was observed in 61% of participants, correlating with higher IL-6 and lower MoCA scores. Thematic analysis identified psychosocial stressors including economic loss and stigma. Clustering revealed inflammatory and neurocognitive phenotypes. Vaccinated individuals exhibited reduced biomarker loads and fewer persistent symptoms. Integrated data pointed to systemic immune dysregulation as a core mechanism of Long COVID. Long COVID represents a biologically grounded, multifaceted syndrome requiring precision diagnostics and multidisciplinary care. The study’s findings support the development of biomarker-based criteria and long-term rehabilitation strategies for affected individuals.
ANTIBIOTIC RESISTANCE IN ENT INFECTIONS: A RETROSPECTIVE STUDY OF PRESCRIBING PATTERNS AND MICROBIAL SHIFTS
In a retrospective analysis of 1,200 ENT infection episodes treated at a tertiary care center from January 2021 to December 2024, we evaluated antibiotic‐prescribing patterns and corresponding shifts in bacterial susceptibility. Although narrow‐spectrum penicillin use declined from 30% of prescriptions in 2021 to 24% in 2024, broad‐spectrum agents—most notably amoxicillin–clavulanate and macrolides—increased from 16% to 23% and from 12% to 21%, respectively. Streptococcus pneumoniae accounted for 42.9% of isolates, followed by Haemophilus influenzae (28.6%) and Staphylococcus aureus (21.4%). Over the study period, macrolide resistance in S. pneumoniae rose from 12% to 28%, and overall non‐susceptibility to amoxicillin–clavulanate climbed from 10% to 25%. Prolonged antibiotic courses were modestly correlated with resistance emergence, and patients of advancing age showed a higher likelihood of harboring resistant organisms. Multivariable logistic regression revealed that broad‐spectrum antibiotic use was independently associated with increased odds of resistance (OR 1.85; 95% CI 1.42–2.41; p < .001). General practitioners contributed the largest share of broad‐spectrum prescriptions, underscoring the need for targeted outpatient stewardship. These findings demonstrate that even moderate shifts toward broad‐spectrum therapies can accelerate resistance among common ENT pathogens. Integrating rapid diagnostic testing, reinforcing adherence to narrow‐spectrum guidelines for uncomplicated cases, and delivering prescriber‐specific feedback on local resistance trends are critical steps to optimize empirical therapy and preserve antibiotic efficacy in otolaryngology.
DEEP RADIOGENOMICS FOR PREDICTING IMMUNOTHERAPY RESPONSE IN NON-SMALL CELL LUNG CANCER: A CROSS-DOMAIN ANALYSIS OF IMAGING, TUMOR MICROENVIRONMENT, AND GENETIC BIOMARKERS
Non-small cell lung cancer (NSCLC) presents a significant clinical challenge in the era of immunotherapy due to its heterogeneous response patterns and the limitations of current predictive biomarkers. This study proposes a comprehensive radiogenomics-based predictive framework integrating 18F-FDG PET/CT imaging, radiomic feature extraction, delta-radiomics, and machine learning to non-invasively predict immunotherapy response. Quantitative radiomic features including SUVmean, SUVmax, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were extracted from baseline and follow-up PET/CT scans across a cohort of NSCLC patients undergoing immune checkpoint inhibitor therapy. Delta-radiomics captured temporal changes in tumor characteristics, while machine learning models—particularly ensemble classifiers—were trained to classify responders versus non-responders with high accuracy. SHAP-based explainable AI was incorporated to identify key imaging biomarkers influencing model predictions. Results showed that delta-radiomic features provided superior predictive power compared to static imaging biomarkers, with models achieving high F1-scores and ROC-AUC values. Volumetric PET parameters also demonstrated significant correlations with PD-L1 expression and progression-free survival, underscoring their clinical relevance. The proposed non-invasive, multimodal approach represents a scalable and interpretable solution for enhancing patient stratification and optimizing immunotherapy outcomes in NSCLC. These findings support the clinical translation of AI-driven radiogenomics tools in personalized cancer care.
MOLECULAR PATHWAYS IN HEAD AND NECK CANCER: UNRAVELING THE GENETIC, EPIGENETIC, AND ENVIRONMENTAL FACTORS CONTRIBUTING TO TUMOR DEVELOPMENT AND METASTASIS
Head-and-neck cancer (HNC), in particular head and neck squamous cell carcinoma (HNSCC type), refers to a heterogeneous malignancy resulting from the convergent and divergent roles played by genetic, epigenetic, and environmental factors. Based on multi-omics analyses in development, recent findings reveal valuable insights about the molecular pathways underlying tumor initiation, progression, and metastasis. This review inspects the key genetic alterations, such as TP53, PIK3CA, and CDKN2A, and epigenetic alterations, including DNA methylation and histone alterations, concerning the tumor microenvironment (TME). An in-depth analysis of the TME is performed, focusing on stroma components, immune cells, and metabolic adjustments support tumor growth and confer resistance to therapy. Understanding these molecular mechanisms will be relevant in the development of precision science approaches aimed at improving the final outcome for the patient. Research on molecular mechanisms of HNSCC development remains essential for creating suitable therapeutic approaches. The tumor's progression as well as its resistance to therapy emerges from the actions of genetic mutations paired with epigenetic changes and TME cellular interactions. The development of precision medicines alongside immunotherapy approaches alongside targeted therapies has created promising new avenues for patient prognostic improvement. Research innovation and cancer biological studies require combined efforts against further issues including tumor heterogeneity alongside therapy resistance. Research findings show that Head and neck cancers (HNCs) create a prominent proportion of worldwide cancer load. The classification of HNSCC stands as the leading form of HNC among its subtypes. These abnormal cell types appear in oral cavity tissues together with those from pharynx and larynx where they demonstrate wide molecular variations and varying clinical behaviors. The pathogenesis of these diseases relies on tobacco use as well as alcohol consumption and infection from particular types of human papillomavirus (HPV). The trio of surgery and radiotherapy and chemotherapy functions as primary treatments for this particular condition yet successful targeted therapies require a deep understanding of tumor development pathways.
Copyright (c) 2025 Roohan Ahmad , Shawal ahmad, Ashraf khan (Author) https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING MODELS FOR PREDICTING PATIENT OUTCOMES IN INTENSIVE CARE UNITS (ICU): A CLINICAL APPROACH
The integration of machine learning (ML) models into Intensive Care Units (ICUs) offers transformative potential for predicting critical patient outcomes and enhancing clinical decision-making. This study developed and evaluated five ML algorithms—Logistic Regression, Random Forest, XGBoost, Support Vector Machine (SVM), and Neural Networks—using a dataset of 25,000 ICU patients from the MIMIC-IV database. The goal was to predict mortality, length of stay (LOS), and 30-day readmission risk. Among all models, XGBoost demonstrated superior performance with an accuracy of 0.87, F1-score of 0.82, and AUC of 0.91 for mortality prediction. SHAP (SHapley Additive exPlanations) analysis identified SOFA score, age, and lactate level as the most influential predictors.For both readmission prediction (AUC = 0.87 >>Calibration tests showed the forecast estimates were precise; Brier score of the XGBoost model was 0.139 and its slope was nearly the same as 1.0. The use of ROC curves, confusion matrices, calibration plots, and SHAP summaries, as well as other graphical representations, helped explain the model, which made it easier to interpret how predictions were developed. The adoption of transparent AI approaches increased comprehension of model logic, making it more useful in clinical rooms and contributing to practitioners’ trust. This study shows that interpretable machine learning models, such as XGBoost, can provide accurate, reliable and actionable predictions that may assist with timely interventions and optimal resource distribution in intensive care units. The findings provide evidence of the capability to embed these interpretable models into realtime ICU workflow and pave the way for future development to federated learning and multi-modal systems.
Copyright (c) 2025 Muhammad Rehan (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING CELLULAR MECHANISMS IN AGING: INSIGHTS INTO TARGETED INTERVENTIONS FOR AGE-RELATED DISEASES
Aging is a complex, multifactorial process driven by cumulative cellular and molecular damage that leads to functional decline and increased susceptibility to chronic diseases. This study investigates the fundamental hallmarks of aging—including genomic instability, mitochondrial dysfunction, epigenetic alterations, and cellular senescence—and evaluates emerging interventions aimed at delaying or reversing age-related deterioration. We identified important senescence triggers like telomere shortening, oxidative stress, and DNA damage that trigger activation of pathways such as p53/p21, NF- Senescent cells contribute to the disruption of tissue function and promotion of inflammation and they were shown to express different genes (e.g., overexpressed p16, higher IL-6, lower SIRT1, and decreased TERT), and to secrete SASP components (e.g., IL-6 and It is widespread to see reduced membrane potential and ATP, increased level of ROS and mtDNA damage in aged cells, all of which are highly correlated to age-related disease intensity. The table and figure analyses revealed that senolytics (Dasatinib + Quercetin), senomorphics (Rapamycin), mitochondrial enhancers (NAD+ precursors) and epigenetic modulators were the most promising treatment classes. Early clinical studies of these treatments suggest a favorable safety and treatment profile. therefore, these agents are advancing in research now. Strong relationships between biomarkers such as 8-OHdG and IL-6 with the development of age-related diseases are indicative of their utility for disease monitoring. Based on a multi-targeted approach targeting the elimination of senescent cells and renewal of mitochondrial function and epigenetic stability, this study claims that aging is modifiable through therapeutic intervention. Moving forward with these didactic procedures could limit long-term worldwide effects of age-related diseases and even double healthspan.
Copyright (c) 2025 Rabia Nasir (Author) https://creativecommons.org/licenses/by/4.0
INNOVATIONS IN VACCINE DEVELOPMENT FOR COMBATING MULTI-DRUG RESISTANT TUBERCULOSIS: EXPLORING NOVEL PLATFORMS, DELIVERY MECHANISMS, AND GLOBAL IMPLEMENTATION STRATEGIES
Multi-drug resistant tuberculosis (MDR-TB) serves as a major global public health threat because it makes treatments difficult while leading to many patient deaths across the world. The Bacillus Chalmette–Guerin (BCG) vaccine has accrued more than 100 years of use but provides weak defense against Mycobacterium tuberculosis strains which show drug resistance in adult patients. The emergence of drug-resistant tuberculosis across populations demands fast-track development of new innovative vaccine solutions which become essential for controlling this spread. Science-based vaccine development for MDR-TB involves studying three novel candidates such as viral vector vaccines, recombinant protein vaccines and BCG constructs. MVA85A VPM1002 and ID93 + GLA-SE represent important vaccine candidates because of their demonstrated ability to improve immune response protection against MDR-TB. Multiple obstacles persist in MDR-TB vaccine research due to weak vaccine performance and difficult trial execution together with emerging resistant bacterial variants. The path to vaccine development becomes more complex because ethical issues together with regulatory constraints appear during the process. The analysis points toward vaccine development pathways which require enhanced adjuvants together with innovative vaccine platforms and worldwide collaboration for creating better vaccine candidates. Additional substantial progress in MDR-TB vaccine development exists but scientists need to solve both research obstacles and distribution framework issues to deliver effective medications to areas where MDR-TB is prevalent. Research together with continuous collaboration remains essential for solving the expanding worldwide threat of MDR-TB. The global public health sector persists in a battle to overcome multi-drug resistant tuberculosis since it represents a primary healthcare emergency. The development of new vaccines for MDR-TB continues to face several years of research before delivering a safe and effective immunization. Recent breakthroughs in vaccine development alongside better comprehension of tuberculosis immunity have brought additional hope to medical research. Future vaccine technology holds promise to establish a necessary instrument which will both control MDR-TB disease and eventually eliminate it altogether. MDR-TB vaccine success hinges on simultaneous scientific, logistical, regulatory obstacles resolution and providing accessibility to high-risk regions.
Copyright (c) 2025 Saad Abdullah, Abdul Ghaffar (Author) https://creativecommons.org/licenses/by/4.0
LEVERAGING MACHINE LEARNING ALGORITHMS FOR PREDICTING DRUG EFFICACY AND TOXICITY IN EARLY DRUG DEVELOPMENT
The high attrition rate, prolonged timelines, and escalating costs of traditional drug development necessitate innovative strategies for early prediction of drug efficacy and toxicity. This study explores the application of nine machine learning algorithms—SVM, Random Forest, XGBoost, DNN, Logistic Regression, Naive Bayes, KNN, AdaBoost, and LightGBM—in predicting drug responses using large-scale datasets such as ChEMBL, PubChem, and Tox21.The greatest AUC- ROC scores, which exceed 0.90 in classifications and regressions, showed the better performance of XGBoost, LightGBM, and Random Forest according to quantitative evaluations. Particularly with XGBoost (IC50 RMSE: With RMSEs for IC50 and EC50 predictions at 0.296, these models showed remarkable accuracy of medication impact prediction. Organ-specific toxicity prediction showed that LightGBM and XGBoost demonstrated excellent performance with over 85% accuracy for many toxicity classes, including hepatotoxicity and cardiotoxicity. The SHAP study revealed some critical molecular factors, that confirmed model predictions, making it more predictable and transparent. In some systems, changes of hyperparameters produced relatively significant changes in model quality (which were up to 15% increase in AUC-ROC score). Scatter plots showed relationships between dataset size and model performance, and further evidence from training time results demonstrated feasibility of these models in high-throughput applications. Notwithstanding such advances, the challenges such as data-variability, interpretability of models limitations, and the need for standardized processes persist. However, as three main considerations demonstrate, this research offers a far-reaching analysis and reveals enormous potential machine learning has to offer to speed up early stage development of drugs and block late phase failures at a lower cost. Such a strategy as developing transparent AI models might contribute to the practical concept of improving efficacy profiling and safety evaluation in future drug development procedures.
Copyright (c) 2025 Shazia Khalid (Author) https://creativecommons.org/licenses/by/4.0
ADVANCEMENTS IN CLINICAL TRIALS: EXPLORING NEW FRONTIERS IN MEDICAL RESEARCH
Clinical trials are undergoing a transformative evolution driven by technological innovations aimed at improving patient outcomes, operational efficiency, and data integrity. With rising complexity in diseases and increasing demand for precision medicine, traditional clinical trial models are no longer sufficient to address the multifaceted challenges of modern research. The integration of digital technologies such as artificial intelligence (AI), wearable devices, blockchain, and decentralized platforms has enabled a paradigm shift toward more adaptive, inclusive, and patient-centered trials.This study explores the implementation of an AI-enhanced clinical trial framework combining machine learning algorithms, real-time data acquisition via wearables, adaptive trial designs, and blockchain-secured data management. A hybrid methodology was adopted, wherein clinical trial simulations incorporated Q-learning models for treatment optimization, mobile health tools for adherence monitoring, and cloud infrastructure for multisite data aggregation and analysis.Results indicate a significant improvement in clinical trial performance across multiple dimensions. Wearable-monitored trials demonstrated higher adherence and retention rates, while AI-supported recruitment and protocol adjustments led to shorter trial durations and enhanced safety monitoring. Ethical compliance improved through the adoption of e-consent platforms and blockchain-based data logging, minimizing privacy breaches and protocol violations. Moreover, personalized treatment regimens supported by AI showed increased response rates in genetically stratified cohorts.In conclusion, the convergence of AI, digital health technologies, and regulatory innovation is reshaping the clinical trial ecosystem. These advancements promote not only methodological efficiency but also ethical accountability and patient empowerment. As this technological synergy continues to evolve, it offers unprecedented potential to accelerate the development of safe, effective, and equitable therapeutic interventions.
Copyright (c) 2023 Sajjad Mehdi , Shazia Khalid (Author) https://creativecommons.org/licenses/by/4.0
THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING DIAGNOSTICS AND PATIENT CARE IN HEALTHCARE SYSTEMS
Artificial Intelligence (AI) has emerged as a transformative force in modern healthcare, offering enhanced precision, speed, and scalability in diagnostics and patient care. The integration of AI technologies—particularly machine learning (ML) and deep learning (DL)—has significantly improved disease detection, treatment personalization, and clinical decision-making across various medical disciplines.This study undertakes a comprehensive analysis of current AI applications in healthcare, focusing on diagnostic imaging, genomics, chronic disease prediction, and personalized treatment planning. The methodology involves a narrative synthesis of peer-reviewed literature from 2020 to 2024, supported by comparative evaluations of AI versus traditional models across multiple clinical domains. Quantitative assessments include model performance in accuracy, sensitivity, precision-recall, and real-time patient monitoring effectiveness.Results demonstrate that AI-driven models consistently outperform traditional diagnostic methods, with convolutional neural networks achieving the highest diagnostic accuracy across radiology, pathology, and genomics. AI systems have also shown significant improvements in chronic disease forecasting, patient monitoring response time, and treatment outcome personalization. Additionally, survey-based metrics indicate strong clinical adoption trends in high-resource settings and increasing patient satisfaction with AI-mediated interventions.Despite these advancements, the study highlights critical challenges, including data privacy concerns, regulatory gaps, and algorithmic bias. Ethical governance and equitable model training remain key issues that need resolution for AI to achieve its full potential in healthcare systems.In conclusion, AI is rapidly redefining diagnostic and therapeutic paradigms by enabling early detection, individualized care, and real-time health monitoring. Continued interdisciplinary collaboration and regulatory alignment are essential to ensure ethical deployment and maximize the societal benefits of AI in healthcare delivery.
Copyright (c) 2023 Jawad Ali , Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING THE IMPACT OF TELEMEDICINE ON HEALTHCARE DELIVERY: BENEFITS, CHALLENGES, AND FUTURE DIRECTIONS
Telemedicine has emerged as a transformative model in modern healthcare, enabling the delivery of medical services remotely through digital platforms. In regions with limited infrastructure and provider availability—such as rural Pakistan—telemedicine holds the potential to bridge long-standing healthcare access gaps. This study investigates the implementation, effectiveness, and challenges of telemedicine adoption within Pakistan’s healthcare system.A mixed-methods approach was employed, combining quantitative analysis of adoption trends, access metrics, satisfaction scores, and cost-efficiency indices with qualitative assessments of regulatory, cultural, and infrastructural barriers. Data were gathered from national health reports, provider surveys, and telemedicine platform usage records. An evaluation framework was developed to calculate the Telemedicine Effectiveness Index (TEI) based on accessibility, cost savings, and care quality.Results indicate a significant improvement in healthcare access, with remote consultation usage increasing steadily from 2015 to 2025. Patient satisfaction was notably higher in urban regions, while cost savings were greatest in rural and remote settings. AI-integrated telemedicine systems demonstrated high diagnostic accuracy for chronic diseases such as hypertension and diabetes. However, the study also revealed persistent challenges including unreliable internet connectivity, limited digital literacy, and regulatory ambiguities that continue to constrain large-scale implementation.The findings underscore the transformative potential of telemedicine to reduce disparities in healthcare delivery, improve system efficiency, and enhance chronic disease management. To fully realize these benefits, Pakistan must invest in robust digital infrastructure, implement comprehensive data privacy laws, and strengthen training programs for both patients and providers. Future telemedicine frameworks should prioritize mobile-based accessibility, integration of AI diagnostics, and culturally tailored community engagement to ensure sustainable, equitable healthcare access for all populations.
Copyright (c) 2023 Uzair Nisar (Author) https://creativecommons.org/licenses/by/4.0
CRISPR-CAS9 BREAKTHROUGHS: TRANSFORMING GENE EDITING AND THERAPEUTIC FRONTIERS
The technology introduced by CRISPR-Cas9 modified molecular biology by providing gene editing in a more precise, quick, and programmable approach. This technology of genome engineering has rapidly moved deployment phases in the course of basic to a rising section of translation medicine. There is no time when genetic disorders, infectious diseases, and cancer have been more promising to be treated. Its ability to cause accurate cuts in the DNA strand in the form of a double-stranded break and assist in controlled gene alteration has transformed the way we diagnose and treat.This paper examines the effectiveness of CRISPR-Cas9 and its variants with high fidelity in various fields and possible real life applications. We employed an entire toolbox to examine the quality of editing, its impact on other genes, the spread of mutations, the spread of variants, and the quality of delivery platforms. To compare the documents and datasets on the preclinical and clinical data, we applied tabular metrics and graphical modeling of the dynamics of editing.It was found that less editing variants such as eSpCas9 and Cas12a cause significantly fewer off-target effects and are superior to editing. Delivery systems in the form of lipid nanoparticles were more efficacious as well as safer, compared to standard vectors. In a mutation profile, it was revealed that the most frequent editing activities were those in insertion and deletions. Nonetheless, tissue-specific editing levels were different across biological systems. Moreover, it was found at the clinical trials that CRISPR-based treatments were increasingly coming into favor, and that their potential was becoming increasingly justified as a course of action in precision medicine.The findings indicate the flexibility of the CRISPR platforms and the effectiveness of their alteration to genomes in a secure, targeting, and effective manner. Of course, there exist ethical issues, particularly, with regard to germline editing, and various distinctions regarding the regulation of such technologies in different countries that necessitate the careful and responsible usage of such. The research concludes that the future of genetic medicine is likely to be transformed with CRISPR-Cas9 but that it must be properly tested and proven to be safe with oversight and be accessible to all both to put its full benefit to society.
Copyright (c) 2023 Abdul Waheed Shah , Abdul Ghaffar (Author) https://creativecommons.org/licenses/by/4.0
ADVANCEMENTS IN PERSONALIZED MEDICINE: INTEGRATING GENOMIC DATA FOR TARGETED TREATMENT STRATEGIES
Personalized medicine has emerged as a transformative paradigm in modern healthcare, aiming to tailor treatments based on individual genetic, environmental, and lifestyle factors. Advances in genomic technologies, including next-generation sequencing and whole-genome sequencing, have enabled clinicians to identify disease-associated mutations and predict drug responses with unprecedented accuracy.This study investigates the integration of genomic data into clinical workflows to enhance diagnostic precision and therapeutic outcomes. Using simulated datasets, the research employs bioinformatics tools and artificial intelligence models to analyze mutation profiles, gene expression levels, and pharmacogenomic interactions across a diverse patient population.The results reveal significant correlations between specific genetic mutations—such as BRCA1, EGFR, and KRAS and differential treatment responses. Elevated expression of oncogenes and higher mutation burdens were associated with poorer clinical outcomes, while gene-specific therapy selection led to improved responsiveness. The study also demonstrates the utility of AI-driven risk stratification in predicting treatment efficacy based on multi-omic profiles.In conclusion, the findings underscore the critical role of genomic profiling in driving personalized healthcare strategies. By leveraging high-throughput sequencing technologies, advanced analytics, and clinical genomics databases, personalized medicine holds the potential to revolutionize disease management. The successful translation of these insights into practice will require interdisciplinary collaboration, ethical oversight, and equitable access to genomic infrastructure worldwide.
Copyright (c) 2023 Saad Abdullah , Younas Rehman (Author) https://creativecommons.org/licenses/by/4.0
BIOTECHNOLOGY IN CANCER IMMUNOTHERAPY: EMERGING APPROACHES AND CLINICAL TRIALS
The integration of biotechnology into cancer immunotherapy has transformed the therapeutic landscape of oncology, offering novel mechanisms for enhancing immune-mediated tumor eradication. This research provides a comprehensive analysis of three principal immunotherapeutic strategies immune checkpoint inhibitors (ICIs), chimeric antigen receptor (CAR) T-cell therapy, and monoclonal antibodies (mAbs)—with emphasis on their clinical efficacy, mechanistic innovation, and biomarker-guided personalization. Utilizing data from recent clinical trials and biotechnology-enabled treatment protocols, the study reveals that PD-1 inhibitors exhibit superior response rates in melanoma and non-small cell lung cancer, while CAR T-cell therapy achieves the highest remission rates in hematologic malignancies, albeit with elevated risks of cytokine release syndrome. Monoclonal antibodies demonstrate consistent performance across HER2-positive and CD20-positive cancers, with emerging bispecific variants enhancing tumor–immune engagement. Quantitative results showed response rates of up to 80% in melanoma (PD-1), long-term remission exceeding 65% in CAR T-treated leukemia patients, and manageable toxicity profiles with advanced mAb constructs. Additionally, biomarker analysis confirmed that high PD-L1 expression and tumor mutational burden (TMB) significantly predict positive therapeutic outcomes. Twelve multi-type visualizations and nine structured tables support the comparative performance and safety profiles across modalities. The findings underscore the pivotal role of biotechnology in enabling personalized immunotherapy and optimizing patient selection. Conclusively, the study highlights that while immunotherapy has redefined cancer treatment paradigms, further innovation is needed to address resistance mechanisms and broaden efficacy in solid tumors. Future strategies integrating artificial intelligence, multi-omics biomarkers, and combination regimens may bridge these gaps, heralding a new era of precision oncology.
Copyright (c) 2023 Hassan Yar Mahsood , Abdul Waheed Shah (Author) https://creativecommons.org/licenses/by/4.0
APPLICATIONS OF GENOMIC SEQUENCING IN PRECISION MEDICINE: REVOLUTIONIZING DIAGNOSTICS AND TREATMENT
Genomic sequencing has emerged as a transformative force in the field of precision medicine, enabling clinicians to analyze the complete genetic blueprint of individuals and tailor medical interventions accordingly. This approach has significantly enhanced diagnostic accuracy, treatment personalization, and the overall effectiveness of healthcare delivery.This study employed a qualitative and data-driven methodology to assess the clinical utility, ethical considerations, and economic implications of integrating genomic sequencing into diagnostic and therapeutic workflows. Through comparative analysis and case studies, we examined its application across oncology, neurology, pharmacogenomics, and rare disease management.The results demonstrated that genomic sequencing markedly improves early disease detection, diagnostic accuracy, and treatment response rates. Mutations such as BRCA1/2, EGFR, and TP53 were frequently identified in cancer patients, leading to more targeted therapies and improved survival outcomes. Additionally, pharmacogenomic insights based on variants in genes like CYP2D6 and TPMT were shown to optimize drug dosing and reduce adverse effects. The integration of sequencing technologies also contributed to a significant reduction in misdiagnosis rates and facilitated the reclassification of complex cases across neurological and rare genetic disorders.Despite these advancements, several challenges remain. Economic constraints, ethical dilemmas surrounding incidental findings, and data privacy concerns continue to limit widespread adoption. However, cost-benefit analyses suggest long-term savings in healthcare expenditures when genomic sequencing is implemented effectively.In conclusion, genomic sequencing represents a pivotal advancement in modern medicine with the potential to redefine diagnostic and treatment paradigms. With appropriate infrastructure, policy frameworks, and ethical safeguards, its broader implementation can significantly enhance healthcare outcomes and pave the way for truly personalized medicine.
Copyright (c) 2023 Saad Abdullah, Rabia Kiran (Author) https://creativecommons.org/licenses/by/4.0
ADVANCEMENTS IN PERSONALIZED MEDICINE: INTEGRATING GENOMIC DATA FOR TAILORED THERAPEUTIC APPROACHES
The integration of genomic data into clinical practice has ushered in a transformative era for personalized medicine, allowing treatment strategies to be tailored based on an individual's genetic profile. This shift from generalized care to precision health has been driven by advancements in high-throughput sequencing technologies, gene-editing tools such as CRISPR-Cas9, and AI-enhanced analytical methods.This study explores the application of genomic technologies across major therapeutic domains, including oncology, cardiovascular diseases, rare genetic disorders, and infectious diseases. Methodologically, the research synthesizes data from large-scale genomic projects and incorporates computational models for risk prediction, variant analysis, and AI-guided therapeutic recommendations. A risk scoring formula was employed to quantify the clinical significance of genetic variants in disease prognosis and treatment planning.The results demonstrate substantial improvements in diagnostic precision and therapeutic efficacy. Specific mutations were successfully linked with targeted therapies in oncology, while pharmacogenomic profiling allowed for individualized drug dosing and minimized adverse reactions in cardiovascular treatments. The implementation of gene therapies in rare disorders showed promising clinical outcomes. Furthermore, the integration of AI facilitated the interpretation of complex genomic datasets, enabling faster and more accurate treatment decisions.The findings affirm that genomic-driven personalization significantly enhances patient outcomes, optimizes resource utilization, and accelerates therapeutic innovation. However, the study also identifies persistent challenges, including ethical concerns, data standardization issues, and unequal global access to genomic healthcare. Addressing these barriers will be critical to realizing the full potential of personalized medicine.In conclusion, the convergence of genomics, computational analytics, and clinical practice represents a pivotal advancement in healthcare. With continued progress in technology, regulatory frameworks, and equity-focused policy initiatives, personalized medicine is poised to become the new standard for precision-driven, patient-centered care.
Copyright (c) 2023 Rizwan Ullah , Syeda Iram Batool (Author) https://creativecommons.org/licenses/by/4.0