Predicting Noise-Induced Hearing Loss among Elderly Residents Near Powerloom Industries Using Machine Learning Vidhya Lekshmi K, Chitra Thara S, Venkateswaramurthy N, Rajkumar J National Journal of Community Medicine, 2026 Background: Environmental noise from small-scale industries, particularly powerloom clusters, is an underrecognized public health concern in India. Older adults in these settings are especially vulnerable due to age-related auditory decline compounded by chronic noise exposure. With expanding semi-urban industrialization and a growing elderly population, noise-induced hearing loss (NIHL) is emerging as a significant yet overlooked health burden. This study estimated the prevalence of NIHL among elderly residents near powerloom industries and evaluated key predictors and machine learning models for community-level screening. Methodology: A community-based cross-sectional study was conducted in Kumarapalayam, Tamil Nadu, among 436 adults aged ≥60 years. Participants were categorized into an exposed group (n = 218; residing <500 m from powerloom units) and a control group (n = 218; residing >2 km away). Environmental noise levels were recorded using standardized sound level meter, showing substantially higher mean daytime noise exposure among the exposed group (77.6 ± 5.67 dB) compared to the control group (52.35 ± 3.95 dB). Hearing thresholds were assessed using validated mobile audiometry. Four ML classification models Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and Logistic Regression were trained and evaluated to predict NIHL from demographic and exposure-related variables. Results: Bilateral hearing loss was markedly higher in the exposed group (65.14%) than in the control group (35.18%). Random Forest demonstrated the strongest performance, achieving an accuracy of 93.4%, a precision of 93.0%, and a recall of 93.2%, outperforming the other models. Predictive variables such as age, proximity to powerloom units, duration of residence, and measured environmental noise levels played significant roles in model performance. Conclusions: Elderly individuals residing near powerloom industries experience significantly greater noise exposure and a correspondingly higher prevalence of NIHL. Machine learning demonstrates strong potential as a practical, field-friendly tool for early identification of at-risk individuals in resource-limited settings.
Effectiveness of a Pharmacist-Guided Digital Learning Program on Prenatal Nutrition Knowledge, Perceptions, and Practices Venkateswaramurthy Venkateswaramurthy N, Krishnamoorthy Krishnamoorthy B, Ramesh Ramesh R, Syed Shah N Current Trends in Biotechnology and Pharmacy, 2025 Maternal malnutrition remains a significant public health concern, particularly in low-resource settings such as India.This quasi-experimental study assessed the impact of a pharmacistguided digital learning program on prenatal nutrition knowledge, perceptions, and practices among 160 pregnant women in Tamil Nadu, India. Participants were divided into intervention (n=80) and control (n=80) groups. The intervention group received an 8-week mobile app–based educational program with weekly pharmacist-led telephonic follow-ups, while the control group received standard antenatal counselling.Post-intervention, the intervention group showed significantly greater improvements in all outcomes: knowledge (+31.8% vs. +5.5%), perceptions (+0.84 vs. +0.15 Likert points), practices (+21.2% vs. +4.1%), and supplement adherence (+2.9 vs. +0.5 points), all with p<0.001. The program’s success is linked to its interactive, culturally tailored content and pharmacist facilitation, grounded in the Health Belief Model.These findings highlight the effectiveness and scalability of integrating digital health tools with pharmacist support to address maternal malnutrition in resource-limited settings and suggest a promising model for enhancing antenatal nutrition education.
Insights into the Impact of Artificial Intelligence on Psoriasis Treatment Strategies: A Mini Review A Prithiviraj, M A Aarthi, N Venkateswaramurthy Indian Dermatology Online Journal, 2025 Psoriasis is a chronic inflammatory skin condition affecting millions of people globally, with prevalence varying significantly between countries. Conventional treatments, including topical agents, phototherapy, and systemic medications, often fail to account for individual variability, leading to suboptimal outcomes and potential adverse effects. Artificial intelligence (AI) has emerged as a promising approach to enhance precision and personalization in psoriasis management, potentially transforming diagnostic accuracy and treatment selection. This review examines the integration of AI across multiple domains of psoriasis treatment: (1) machine learning algorithms for phototherapy outcome prediction, (2) deep learning techniques for lesion segmentation and severity assessment, (3) AI-enhanced remote photographic monitoring systems, and (4) predictive modeling for response to systemic therapies and biologics. The analysis encompasses various AI methodologies, including random forest classifiers, convolutional neural networks, multiscale superpixel clustering, and gradient-boosted decision trees applied to clinical datasets, imaging analysis, and multi-omic patient data. AI-driven models demonstrate significant clinical utility with phototherapy outcome prediction, achieving high sensitivity (>84%) and accuracy (75-85%). Automated lesion segmentation reaches 86.99%-pixel accuracy, while remote AI assessments strongly correlate with clinical evaluations (Intraclass Correlation Coefficient [ICC] = 0.78-0.99). Notably, predictive models can forecast biologic therapy responses with > 95% accuracy within 2-4 weeks of treatment initiation, substantially reducing evaluation timelines from the conventional 12-week assessment period. AI technologies offer transformative potential in psoriasis management by enabling precise diagnosis, outcome prediction, and personalized therapy selection. Current implementations show promising results across diverse clinical applications, from phototherapy optimization to biologic response prediction. While challenges in dataset diversity, standardization, and validation remain, these represent opportunities for further advancement toward precision medicine in dermatology.
The Integration of Artificial Intelligence in Hormone Analysis: Transforming Diagnostic Precision and Personalized Endocrine Care , Sreedhar Manikandan, Sudharsan Selvam, , N. Venkateswaramurthy, and Endocrinology Research and Practice, 2025 Traditional hormone analysis methods are often limited by single-point measurements, assay vari ability, and biological fluctuations that reduce diagnostic precision. Artificial intelligence (AI) offers powerful tools to address these limitations by recognizing complex hormone patterns, predict ing physiological events, and guiding personalized treatment strategies. This review explores how AI enhances endocrine diagnostics across metabolic, reproductive, thyroid, and adrenal hormone domains. By integrating vast temporal datasets and interpreting subtle variations often missed by conventional methods, AI facilitates earlier detection of disorders such as diabetes, polycystic ovary syndrome (PCOS), thyroid dysfunction, and adrenal abnormalities. It also supports dose optimization and real-time monitoring. Artificial intelligence–driven tools are evolving to model multi-hormone systems, offering a holistic understanding of endocrine function and aiding clinical decision-making. The integration of AI into hormone analysis signifies a paradigm shift toward proactive, precise, and personalized endocrine care. Cite this article as: Selvam S, Manikandan S, Venkateswaramurthy N. The integration of artificial intelligence in hormone analysis: transforming diagnostic precision and personalized endocrine care. Endocrinol Res Pract. 2025;29(4):356-364.
Enhanced Detection of Gastrointestinal Malignancies using Machine Learning-Optimized Liquid Biopsy: A Mini Review Shankar Ganesh M., Venkateswaramurthy N. Current Cancer Drug Targets, 2025 Background: Gastrointestinal (GI) cancers represent some of the most common and lethal malignancies globally, underscoring the urgent need for improved diagnostic strategies. Traditional diagnostic methods, while effective to some degree, are often invasive and unsuit-able for regular screenings. Objective: This review article explores integrating machine learning (ML) with liquid biopsy techniques as a revolutionary approach to enhance the detection and monitoring of GI cancers. Liquid biopsies offer a non-invasive alternative for cancer detection through the analysis of circulating tumor DNA (ctDNA) and other biomarkers, which when combined with ML, can significantly improve diagnostic accuracy and patient outcomes. Methods: We conducted a comprehensive review of recent advancements in liquid biopsy and ML, focusing on their synergistic potential in the early detection of GI cancers. The review addresses the application of next-generation sequencing and digital droplet PCR in enhancing the sensitivity and specificity of liquid biopsies. Results: Machine learning algorithms have demonstrated remarkable ability in navigating complex datasets and identifying diagnostically significant patterns in ctDNA and other circu-lating biomarkers. Innovations such as machine learning-enhanced "fragmentomics" and tomographic phase imaging flow cytometry illustrate significant strides in non-invasive cancer diagnostics, offering enhanced detection capabilities with high accuracy Conclusion: The integration of ML in liquid biopsy represents a transformative step in the early detection and personalized treatment of GI cancers. Future research should focus on overcoming current limitations, such as the heterogeneity of tumor-derived genetic materials and the standardization of liquid biopsy protocols, to fully realize the potential of this technol-ogy in clinical settings.
Artificial Intelligence (AI) Generated Health Counseling For Mental Illness Patients Shankar Ganesh M, Venkateswaramurthy N Current Psychiatry Research and Reviews, 2025 Background: Mental illness remains a global public health concern, affecting millions of individuals worldwide. However, barriers such as limited access to mental healthcare, stigma, and resource constraints hinder effective interventions and treatment. The fourth industrial age, marked by the integration of artificial intelligence technologies, offers innovative solutions to revolutionize mental health counseling and support. Method: This review explores the challenges faced in traditional mental healthcare and proposes the integration of AI-generated health counseling as a transformative approach. AI-powered chatbots and virtual assistants present accessible, cost-effective alternatives that overcome geographical barriers and combat stigma. These chatbots employ natural language processing and machine learning to engage users in personalized and interactive conversations. Chatbots also offer continuous support, psychoeducation, and coping strategies. Virtual Reality Therapy leverages AI to create realistic simulations for exposure therapy, proving effective in treating anxiety disorders and PTSD. AI-driven voice assistants and virtual coaches enhance mental health counseling by delivering behavioral therapy and improving symptoms of depression and anxiety. Results: They enhance accessibility, provide 24/7 support, and reduce stigma, offering personalized support tailored to individual needs. Integrating AI-generated health counseling in mental healthcare can bridge treatment gaps, improve accessibility, and strengthen the patient-provider relationship. Conclusion: AI serves as a valuable supplement, working collaboratively with human therapists to provide comprehensive care. Embracing AI technologies responsibly holds promise for the future of mental health counseling and offers transformative possibilities to address the global burden of mental illness.
FABRICATION OF LEVOFLOXACIN-LOADED PH-SENSITIVE EUDRAGIT POLYMERIC FLOATING MICROBALLOON BIOMATERIAL FOR GASTRORETENTIVE DRUG DELIVERY Manivasakam Prakash, Venkateswaramurthy Nallasamy, Senthil Venkatachalam Journal of Applied Pharmaceutical Research, 2025 Background: The design of improved biomaterials for medication administration is vital in overcoming problems associated with standard therapy for Helicobacter pylori (H. pylori)-induced stomach ulcers. This study aims to develop and characterize floating biomaterial of levofloxacin microballoon biomaterials based on a fluoroquinolone-benzoxazine system conjugated with methylated piperazine and carboxylic acid groups, strategically designed for prolonged gastric delivery. Methodology: Using the emulsion solvent diffusion method, thirteen preparations were developed by different polymer ratios (pH-sensitive Eudragit RS-100 and Ethyl Cellulose), stirring speeds, and temperatures. Results and Discussion: In the buoyancy study simulated gastric fluid (pH 1.2), the best formulation (F9) shows superior encapsulation efficiency (90.2%) and sustained drug release profile (91.2% over 8 hours) that increases its effectiveness against H. pylori. FTIR and SEM analyses conducted during characterization studies verified the drug stability and the spherical microballoon morphology, with a particle size of 81.2 µm. Levofloxacin-loaded microballoon biomaterials provide a unique gastro-retentive delivery system that improves patient compliance, reduces off-target effects, and maintains effective drug concentrations at the infection site, thereby strengthening the therapeutic efficacy of levofloxacin against H. pylori. Conclusion: This creative method offers a viable substitute for traditional therapies for stomach ulcers and is consistent with the overarching objectives of targeted delivery systems and structure-based drug development.
AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies Chitra Thara S., Vidhya Lekshmi K., Venkateswaramurthy N. Current Aging Science, 2025 Hearing loss is a prevalent condition affecting over 500 million people globally, with projections estimating more than 700 million cases by 2050. Artificial intelligence (AI) holds transformative potential in audiology, enhancing diagnostic, therapeutic, and rehabilitation outcomes. This review explores the applications of AI in hearing aids, cochlear implants, sign language recognition, and tele-audiology. A comprehensive literature review was conducted using PubMed, Google Scholar, and other academic databases. Relevant studies on AI-driven advancements in audiology were analyzed, focusing on hearing aid technologies, cochlear implants, diagnostics, and tele-audiology platforms. AI technologies significantly enhance hearing aids through real-time personalization and adaptive algorithms. Cochlear implants leverage AI for improved speech recognition and listening comfort. AI-powered sign language systems facilitate communication through real-time gestureto- text conversions, while tele-audiology expands care access using AI-enabled platforms. Diagnostic advancements include AI-enhanced audiometric testing and otoscopy. AI is revolutionizing hearing healthcare by providing personalized, efficient, and accessible solutions. Its integration into audiology represents a paradigm shift, offering significant improvements in patient outcomes and quality of life.
Preterm birth facts: A review S.M. Vanmathi, M. Monitha Star, N. Venkateswaramurthy, R. Sambath Kumar Research Journal of Pharmacy and Technology, 2019
Role of chloroquine as an anticancer agent Parvathy R Panicker, Sudha M, Venkateswaramurthy N, Sambathkumar R International Journal of Research in Pharmaceutical Sciences, 2018
Impact of environmental factors as an etiology for diabetes mellitus Journal of Pharmaceutical Sciences and Research, 2017
Assessment of drug prescription pattern in paediatric patients Journal of Pharmaceutical Sciences and Research, 2017
Statins: Pleiotropic effect A Ashna, S Jeena, PV Vidhya, N. Venkateswaramurthy, R. Sambathkumar Research Journal of Pharmacy and Technology, 2016
A study on impact of clinical pharmacist interventions on relationship between treatment satisfaction and medication adherence in hypertensive patients Journal of Pharmaceutical Sciences and Research, 2016
Patient education: Impact of pharmacists in providing patient education in asthma patients Journal of Chemical and Pharmaceutical Sciences, 2016
Formulation of clarithromycin loaded mucoadhesive microspheres by emulsification-internal gelation technique for anti-Helicobacter pylori therapy International Journal of Pharmacy and Pharmaceutical Sciences, 2011
Preparation and evaluation of mucoadhesive microspheres containing heparin for antiulcer therapy Research Journal of Pharmacy and Technology, 2011
Formulation and evaluation of stomach specific amoxicillin loaded mucoadhesive microspheres Iranian Journal of Pharmaceutical Sciences, 2010
Formulation and in vitro evaluation of furazolidone mucoadhesive microspheres International Journal of Pharmacy and Pharmaceutical Sciences, 2010
Invitro cytotoxic effect of ethanolic extract of pseudarthria viscida linn International Journal of Pharmacy and Pharmaceutical Sciences, 2010
Formulation and evaluation of Clarithromycin loaded mucoadhesive microspheres for Anti-Helicobacter pylori effect Research Journal of Pharmaceutical Biological and Chemical Sciences, 2010
Formulation and evaluation of mucoadhesive microspheres of amoxicillin trihydrate by using Eudragit RS 100 International Journal of Chemtech Research, 2010
