International Journal of Food Safety and Public Health
International Journal of Food Safety and Public Health (AJB), ISSN 2756-3693 is a Open Access, Fully Refereed and Peer Reviewed International Journal. Notably, it is a Referred, Highly Indexed, Online International Journal . IJFSPH is published as a Monthly Journal with 12 issues per year. We also host International and National Conferences to publish their research work. IJFSPH covers all disciplines including Arts, Science, Commerce, Social-Sciences, Management and Engineering. IJFSPH always strives to be a platform for Academicians, new Researchers, Authors, Engineers, Technocrats and Engineering Scholars. Since inception, IJFSPH is continuously publishing original and best quality research articles. The Journal was under International Scholars Journal (ISJ PUBLISHING COMPANY) Till December 2024.
Traditional Medicinal Knowledge And Herbal Healthcare Practices Among Tribal Communities Of Kanker, Chhattisgarh
Kanker district, situated in the Bastar plateau of Chhattisgarh, harbors a dense mixed-deciduous forest cover and a predominantly tribal population Gond, Halba, Bhatra and Abujhmaria communities among others whose primary healthcare still leans heavily on plant-based remedies. This review consolidates published ethnobotanical work relating to Kanker and its immediate ecological neighborhood (Bastar, Kondagaon, Dhamtari and adjoining Surguja pockets) to build a composite picture of what has actually been documented, where the documentation is thin, and how consistent the reported plant uses are across independent field surveys. Sixty-nine records were initially retrieved from academic databases and institutional repositories; after removing duplicates and papers with no primary field component, twenty-seven studies met the inclusion criteria, of which nine dealt specifically with Kanker or bordering blocks. Across these, over 210 plant species spanning roughly 70 families were recorded in medicinal use, with Fabaceae, Euphorbiaceous and Competencies recurring as the most cited families. Leaves and roots dominate as the plant parts harvested, and gastrointestinal disorders, skin ailments, fever and snakebite emerge as the ailment categories with the highest informant agreement. The synpaper also surfaces recurring methodological weaknesses small informant samples, absence of voucher specimen deposition, inconsistent use of quantitative indices such as informant consensus factor and fidelity level, and negligible attention to conservation status. The paper closes by outlining a research agenda for future Kanker-focused fieldwork, arguing that standardized quantitative ethnobotany combined with phytochemical validation is overdue for this particular pocket of central India.
Ethnobotany; Kanker district; Chhattisgarh; medicinal plants; tribal healthcare; traditional knowledge; quantitative ethnobotany
Recent Advances in Indole-Based Heterocyclic Compounds as Antibacterial and Antifungal Agents: Synthetic Strategies, Structure–Activity Relationships and Therapeutic Perspectives
Antimicrobial resistance (AMR) has emerged as one of the greatest challenges to global healthcare, significantly reducing the effectiveness of existing antibacterial and antifungal drugs. The increasing prevalence of multidrug-resistant bacteria and pathogenic fungi has accelerated the search for novel therapeutic agents with improved efficacy and safety. Among the various heterocyclic scaffolds explored in medicinal chemistry, the indole nucleus has gained considerable attention because of its unique structural characteristics, ease of functionalization, and broad spectrum of biological activities. Indole-based heterocyclic compounds have demonstrated remarkable antibacterial and antifungal properties by targeting multiple microbial pathways, including DNA replication, cell-wall synthesis, membrane integrity, and ergosterol biosynthesis. Recent advances in synthetic chemistry, molecular hybridization, molecular docking, and computational drug design have facilitated the development of potent indole derivatives with enhanced pharmacological profiles. This review summarizes the chemistry of indole, major synthetic approaches, antimicrobial activities, structure–activity relationships (SAR), molecular docking studies, ADMET considerations, and future prospects for indole-based antimicrobial agents.
Indole, Heterocyclic compounds, Antibacterial activity, Antifungal activity, Medicinal chemistry, Structure–Activity Relationship, Molecular docking
Public Expenditure On Education And Health And Its Impact On Inequality: Household Evidence From Lucknow District, Uttar Pradesh
Public spending on education and health is the largest instrument of redistribution available to an Indian district administration, yet its distributional consequences are almost never measured at the district scale at which the services are actually delivered. This paper estimates how in-kind provision of schooling and medical care alters consumption inequality in Lucknow district, Uttar Pradesh. A stratified two-stage survey of 1,456 households drawn from 108 primary sampling units across the eight rural development blocks and four municipal zones is used to value the education and health services each household actually consumes, at government unit cost, and to add that value to monthly per capita consumption expenditure. In-kind provision is worth Rs 363 per capita per month, or 6.02 per cent of mean consumption, and it is strongly pro-poor in absolute terms: the poorest quintile receives Rs 500, equivalent to 19.54 per cent of its own consumption, against Rs 196 and 1.70 per cent for the richest. The concentration indices are negative for both services, and the Kakwani indices of −0.487 for education and −0.461 for health confirm that each is distributed more equally than consumption itself. Valuing the services reduces the Gini coefficient from 0.2965 to 0.2711, a fall of 2.54 points or 8.55 per cent, of which reranking absorbs only a negligible part. Recentred influence function regressions with stratum fixed effects and standard errors clustered by sampling unit confirm the association, while an instrumental-variables specification using distance to the nearest facility is too imprecise to be informative. A Monte Carlo replication of 200 independent survey draws establishes which of these findings are robust and which are artefacts of a single sample.
public expenditure; benefit incidence analysis; consumption inequality; in-kind transfers; Lucknow district; recentred influence function; household survey
Comparative Analysis Of Ai Challenges In Healthcare: Sikar And Bahrain
This study presents a comprehensive comparative analysis of artificial intelligence (AI) challenges in healthcare systems between Sikar district, India, and Bahrain. The research employs a quantitative approach utilizing secondary data analysis from healthcare statistics, government reports, and academic databases spanning 2022-2024. The primary objective is to identify, compare, and analyze the distinct challenges faced by both regions in implementing AI technologies in healthcare delivery. The methodology incorporates statistical analysis of healthcare infrastructure data, AI adoption rates, and implementation barriers across both jurisdictions. Key findings reveal significant disparities in digital infrastructure readiness, with Bahrain demonstrating 89% digital health coverage compared to Sikar's 34%. Healthcare professional-to-population ratios show Bahrain at 0.92 doctors per 1,000 people versus India's national average of 0.64 per 1,000. AI implementation challenges include data quality issues (67% in Sikar vs. 23% in Bahrain), infrastructure limitations (78% vs. 31%), and regulatory barriers (45% vs. 19%). Results indicate that while Bahrain faces primarily technical and ethical challenges, Sikar confronts fundamental infrastructure and resource constraints. The discussion emphasizes the need for region-specific AI implementation strategies addressing local healthcare contexts. In conclusion, successful AI integration requires tailored approaches considering economic development levels, existing healthcare infrastructure, and regulatory frameworks specific to each region's unique challenges and opportunities.
Artificial Intelligence, Healthcare Challenges, Digital Health, Comparative Analysis, Healthcare Infrastructure
Nanotechnology-Enabled Solutions for Sustainable Healthcare and Environmental Protection
The convergence of escalating healthcare challenges and pervasive environmental degradation represents one of the most formidable crises confronting humanity in the twenty-first century (Sung et al., 2021; Hanahan, 2022). Conventional therapeutic and remediation strategies are increasingly constrained by issues of toxicity, inefficiency, lack of specificity, and unsustainable resource consumption (Rabiee, 2025a; Budhwar et al., 2025). In this context, nanotechnology-enabled solutions have emerged as a transformative paradigm, offering unprecedented opportunities to address these interconnected challenges through innovative, sustainable, and multifunctional approaches (Bayda et al., 2020; Khan et al., 2022). We posited that the strategic design and engineering of nanomaterials with precisely tunable physicochemical properties would enable the development of next-generation platforms for targeted drug delivery, precision diagnostics, and environmental remediation (Parveen et al., 2022; Das et al., 2023). To test this hypothesis, we deployed a comprehensive multidisciplinary strategy integrating rational nanomaterial design, sustainable synthesis protocols, rigorous physicochemical characterization, advanced biological evaluation, and environmental performance assessment (Sharifi et al., 2022; Kaur et al., 2023). Our integrated discovery pipeline seamlessly wove together principles of green chemistry, precision engineering, state-of-the-art spectroscopic and microscopic characterization, and mechanistic elucidation through both experimental and computational approaches (Duan et al., 2023; Yadav et al., 2024). This comprehensive approach yielded seminal achievements across multiple domains: the identification of multifunctional nanocarriers exhibiting exceptional targeted drug delivery efficacy with remarkable biocompatibility; the development of highly sensitive nanosensors for early disease detection; and the creation of advanced photocatalytic nanomaterials demonstrating outstanding efficiency for the degradation of persistent environmental pollutants (Zhou et al., 2023; Liu et al., 2024). Notably, these nanotechnology-enabled platforms demonstrate compelling multifunctionality—simultaneously achieving therapeutic efficacy, diagnostic capability, and environmental remediation functionality through integrated design strategies (Zhang et al., 2023; Wang et al., 2024). Beyond introducing highly promising nanomaterials for biomedical and environmental applications, this study delivers a decisive structural and mechanistic roadmap for the rational design and optimization of nanotechnology enabled solutions (Chen et al., 2023; Ahmed et al., 2024). It decrypts the fundamental structure-activity relationships governing nanomaterial efficacy and delineates a clear path for the advanced translational development of these compelling sustainable nanotechnology platforms (Huang et al., 2023; Singh et al., 2024).
Nanotechnology, Sustainable Healthcare, Environmental Protection, Targeted Drug Delivery, Nanosensors, Photocatalysis, Multifunctional Nanomaterials, Green Synthesis, Theranostics, Environmental Remediation, Precision Medicine, Biocompatibility
A Hybrid Cloud Storage Architecture for Large-Scale Medical Data
The rapid digitization of healthcare services and the widespread adoption of Electronic Health Records (EHRs), medical imaging systems, and clinical information platforms have resulted in an unprecedented growth in medical data. Traditional storage infrastructures often struggle to provide the scalability, availability, security, and cost efficiency required for managing such large and continuously expanding datasets. Hybrid cloud computing has emerged as a practical solution by combining the reliability of private cloud environments with the scalability and resource flexibility of public cloud services. This paper presents a Hybrid Cloud Storage Architecture designed for large-scale medical data management. The proposed architecture integrates secure data acquisition, intelligent data classification, distributed storage management, encryption, and access control mechanisms to ensure reliable storage and efficient retrieval of clinical information. Frequently accessed and highly confidential patient records are maintained within the private cloud, whereas large archival datasets and non-critical medical information are stored in the public cloud to improve storage utilization and reduce operational costs. The architecture also incorporates data synchronization, backup management, and disaster recovery mechanisms to maintain service continuity and information integrity. The proposed framework enhances storage efficiency, supports secure healthcare data sharing, and provides a scalable infrastructure capable of meeting the increasing storage demands of modern healthcare organizations.
Hybrid Cloud Computing, Medical Data Storage, Electronic Health Records (EHR), Healthcare Cloud, Medical Image Storage, Cloud Security, Distributed Storage, Healthcare Informatics, Data Encryption, Cloud Architecture
Privacy-Preserving Access Control for Electronic Health Record Systems
The rapid adoption of Electronic Health Record (EHR) systems has significantly improved the management, storage, and sharing of patient medical information among healthcare providers. However, ensuring the privacy and confidentiality of sensitive health records remains a critical challenge due to increasing concerns regarding unauthorized access, data breaches, and regulatory compliance. Privacy-preserving access control mechanisms have emerged as an effective solution to safeguard patient information while maintaining secure and efficient data accessibility. This paper presents an overview of privacy-preserving access control techniques employed in Electronic Health Record systems, emphasizing role-based access control, attribute-based access control, encryption-based access policies, and patient-centric authorization models. The study discusses the importance of secure authentication, fine-grained authorization, and cryptographic approaches for protecting medical data against unauthorized disclosure. Furthermore, existing challenges, including scalability, interoperability, emergency access, and policy management, are examined. The proposed framework highlights the integration of privacy-preserving mechanisms with access control strategies to improve confidentiality, integrity, and availability of healthcare information. The study concludes that combining cryptographic techniques with flexible access control models can significantly enhance the security and privacy of Electronic Health Record systems while supporting efficient healthcare services.
Electronic Health Records (EHR), Privacy Preservation, Access Control, Healthcare Security, Role Based Access Control, Attribute-Based Encryption, Patient Privacy, Data Confidentiality, Authentication, Healthcare Information Systems
Operational And Behavioural Gaps In Mosquito Vector Control In A Forest-Rural Block Of Central India: A Household And Breeding- Habitat Assessment From Devbhog, Chhattisgarh
Background: Mosquito-borne diseases remain a persistent public-health concern in forest-rural landscapes where ecological suitability for vectors overlaps with operational constraints and household-level behavioural gaps. Devbhog Block of Gariyaband district, Chhattisgarh, represents such a setting, with scattered rural settlements, agricultural fields, domestic water-storage practices and forest-fringe habitats. This study assessed vector-control coverage, environmental risk, breeding-habitat distribution, simple larval observations and community knowledge, attitude and practices (KAP) in selected villages of Devbhog Block. Methods: A descriptive cross-sectional field assessment was conducted in 10 selected villages. Data were collected from 100 households using a structured survey, transect walks, environmental observations, simple larval assessment and keyinformant interactions with local health personnel. The analysis used frequencies and percentages to describe household risk factors, intervention coverage, breeding habitats, seasonal variation and KAP indicators. Results: Household-level environmental risk was prominent: poor drainage was recorded around 78% of households, uncovered water storage around 69% and stagnant water around 64%. Indoor residual spraying (IRS) coverage was 46%, while complete spraying acceptance was 32%. Mosquito-net ownership was 66%, but regular use was only 37%, suggesting a large ownership-use gap. Larval source management was weak; only 22% of households reported removing stagnant water and 16% participated in cleanliness drives. Eighty-seven potential breeding sites were recorded, dominated by stagnant ponds/ditches, forest pools, domestic containers and paddy fields. Simple larval observations identified Anopheles spp. (41.9%), Aedes spp. (33.8%) and Culex spp. (24.3%). KAP indicators were low, particularly for dengue transmission, breeding-site awareness and community source reduction. Conclusion: Devbhog Block requires locally adapted integrated vector management that combines improved IRS planning, consistent mosquito-net use, household-level source reduction, seasonal surveillance, Panchayat-supported sanitation and ASHA/Mitanin-led communication. The findings highlight that vector-control effectiveness in forest-rural settings depends as much on community behaviour and environmental management as on the availability of formal control measures.
Devbhog Block; vector control; malaria; dengue; mosquito breeding; KAP; larval source management; Integrated Vector Management; Chhattisgarh.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Diagnostic Accuracy Through Generative Ai And Synthetic Data Generation For Robust Medical Imaging
The scarcity, heterogeneity, and limited availability of high-quality annotated medical imaging data continue to pose a significant challenge in developing robust and generalizable deep learning models for clinical applications. In real-world healthcare scenarios, acquiring large-scale labeled datasets is difficult due to privacy regulations, high annotation costs, and the requirement of expert radiologist involvement. These limitations often result in models that lack generalization and perform poorly on unseen or rare disease cases. To address this issue, this project proposes a Multi-Domain Generative Adversarial Network (MD-GAN) framework designed specifically for cross-organ synthetic medical image synthesis. The proposed system is trained on multiple imaging modalities, including Brain MRI and Lung CT/X-ray datasets, enabling it to learn both shared anatomical structures and modality-specific pathological variations. The architecture incorporates a shared feature extraction backbone that captures common medical patterns across domains, while organspecific conditioning modules ensure that unique disease characteristics are preserved and accurately represented. Through adversarial training between the generator and discriminator, the model produces highly realistic synthetic medical images that can effectively augment existing datasets. Experimental results demonstrate that incorporating MD-GAN-generated synthetic data significantly enhances downstream medical image segmentation performance, particularly improving the Dice Similarity Coefficient (DSC) when compared to models trained exclusively on real-world data. This confirms the effectiveness of the proposed approach in improving diagnostic accuracy and model robustness in medical imaging applications.
Generative Adversarial Networks (GAN), Medical Imaging, Synthetic Data, MD-GAN, Data Augmentation, Deep Learning.
Mr. Mohd Ismail Iqbal, Mr. Syed Muqeet Ahmed, Mr. Syed Nouman Ali
With the rapid advancement of Artificial Intelligence and digital healthcare technologies, automated systems are increasingly being used to provide medical assistance and information. However, many people still face challenges in accessing immediate healthcare guidance, especially for basic medical queries and preliminary diagnosis. A Healthcare Chatbot System offers an efficient solution by providing instant responses, basic medical advice, and health-related information through an interactive conversational interface. This research paper presents the development of a Healthcare Chatbot System that uses Natural Language Processing (NLP) and machine learning techniques to understand user queries and provide relevant healthcare responses. The system is designed to assist users in identifying possible symptoms, suggesting precautionary measures, and guiding them toward appropriate medical consultation when necessary. It integrates a structured medical knowledge base to ensure reliable and accurate information delivery.
Healthcare Chatbot, Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Digital Healthcare, Symptom Analysis, Medical Assistance, Conversational AI, Clinical Decision Support, Health Informatics, Automated Diagnosis Support, Patient Engagement
Mr. Mohammed Omar Khan, Mr. MD Azeem, Mr. Mirza Mohammed Baig
Formulation Of Novel 1,3,4-Oxadiazole Entities And Examination Of Their Antioxidant Efficiency
The 1,3,4-oxadiazole nucleus is a privileged five-membered heterocyclic scaffold with sustained relevance in medicinal chemistry because of its bioisosteric character and capacity to stabilise radical intermediates. The present work was framed to formulate a novel series of 2,5-disubstituted 1,3,4-oxadiazole entities (OX-1 to OX-8) bearing electron-donating and electron-withdrawing substituents, and to assess their antioxidant competence. Aromatic acid hydrazides were synthesised through Fischer esterification, hydrazinolysis, and subsequent cyclodehydration with phosphorus oxychloride (POCl₃) at reflux. Structures were verified through FT-IR, ¹H NMR, ¹³C NMR, and LC-MS. Antioxidant potential was examined through DPPH, ABTS, hydrogen peroxide (H₂O₂), nitric oxide (NO), and ferric reducing antioxidant power (FRAP) assays, with ascorbic acid as reference. Results indicated that OX-6, carrying a 4-hydroxy-3-methoxyphenyl group, exhibited the strongest efficiency (DPPH IC₅₀ = 14.82 μg/mL; ABTS IC₅₀ = 11.46 μg/mL; FRAP = 1187 μM Fe²⁺/g). Hypothesis-testing by one-way ANOVA confirmed substituent-dependent differences (p < 0.05). Electron-donating hydroxyl and methoxy groups markedly enhanced radical scavenging, while nitro and halogen groups reduced activity. The study concludes that properly substituted 1,3,4-oxadiazoles are valuable candidates for antioxidant drug development.
1,3,4-Oxadiazole; Cyclodehydration; DPPH scavenging; ABTS assay; Structure–activity relationship
Breast Cancer Detection And Classification Using Machine Learning
Breast cancer is one of the most prevalent and life-threatening diseases affecting women globally. Early detection and accurate classification are crucial for improving survival rates and reducing mortality. This research paper presents a machine learning-based approach for the detection and classification of breast cancer tumors into benign and malignant categories. The proposed system utilizes supervised learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Decision Trees, and Random Forest to analyze medical data. The system is trained and evaluated using the Wisconsin Breast Cancer Dataset, which contains features extracted from digitized images of fine needle aspirates of breast masses. Data preprocessing techniques such as normalization, feature scaling, and handling missing values are applied to improve model performance. The results indicate that ensemble models like Random Forest achieve higher accuracy compared to traditional classifiers. The proposed system aims to assist healthcare professionals by providing a reliable and efficient diagnostic tool, thereby reducing human error and enabling early-stage detection of breast cancer.
Breast Cancer, Machine Learning, Random Forest, Classification, Medical Diagnosis, Supervised Learning, Early Detection, Healthcare AI.
Mr. Mohd Najeeb Murtuza, Mr. Mohammad Anwar Uddin, Mr. Mohd Ayan Sadiq
Elderly Fall Detection Using Deep Learning Techniques
Elderly Fall Detection Using Deep Learning Techniques is an intelligent healthcare monitoring system designed to automatically detect fall incidents in real time. Falls are one of the leading causes of serious injuries among older adults, often resulting in fractures, hospitalization, or life-threatening complications. The proposed system utilizes deep learning models to analyze video input and identify abnormal human postures and sudden movements associated with falls. Convolutional Neural Networks (CNN) and temporal learning models are employed to extract spatial and motion-based features from video frames. The system distinguishes between normal daily activities such as walking, sitting, or bending and actual fall events with high accuracy. When a fall is detected, the system immediately generates an alert notification to caregivers or family members, ensuring quick assistance. This approach enhances elderly safety, reduces response time in emergencies, and supports independent living through continuous and automated monitoring.
Elderly Fall Detection, Deep Learning, Convolutional Neural Networks (CNN), Human Activity Recognition, Video Surveillance, Healthcare Monitoring, Real-Time Alert System, Motion Analysis, Smart Healthcare, Assistive Technology.
Mr.Mohammed Shabbir Ali, Mr. Mohammed Waleed Mohiuddin, Mr.Mohammed Abdul Arfath Sayeed, Mr.Mohammad Haroon Baig
Leveraging Deep Learning For Early Detection And Intervention In Diabetic Retinopathy Through Fundus Photography
Diabetic Retinopathy (DR) remains one of the leading causes of preventable blindness among diabetic patients worldwide. Early detection through expert examination of fundus photographs is critical but time-consuming, expensive, and inaccessible in rural areas. This research proposes an automated deep learning-based system leveraging Convolutional Neural Networks (CNNs) for early detection and multi-stage classification of Diabetic Retinopathy using retinal fundus photography. The system employs advanced image preprocessing techniques including noise reduction, contrast enhancement, and normalization to improve diagnostic accuracy. Feature extraction and hierarchical analysis enable the identification of critical markers including microaneurysms, hemorrhages, and exudates. The proposed CNN architecture demonstrates exceptional performance in classifying DR severity levels with high precision and recall. Evaluation metrics including accuracy, sensitivity, specificity, and F1-score validate the clinical reliability of the system. Results demonstrate that automated screening can significantly reduce healthcare burden, improve diagnostic efficiency, and enable large-scale deployment in resource-limited settings, ultimately contributing to the prevention of diabetes-related blindness worldwide.
Diabetic Retinopathy, Deep Learning, Convolutional Neural Network (CNN), Fundus Images, Medical Image Processing, Automated Diagnosis, Early Detection, Image Preprocessing, Multi-Class Classification, Computer-Aided Diagnosis, Healthcare AI, Retinal Disease Detection, Feature Extraction, Classification Accuracy, Telemedicine Screening
Syed Inzemam Hussain, Farhaan Jameel, Mohammed Faraaz
Predictive Modeling Of Drug Safety Using Adverse Drug Reaction Data
Monitoring drug safety is important to protect patients, but traditional systems are slow because they rely on manual reporting and looking at data after problems occur. This study uses machine learning to quickly analyze large datasets like FDA FAERS and WHO VigiBase to detect harmful drug reactions earlier. By processing data such as patient details, drug information, and past reactions, models like decision trees, SVMs, and neural networks can predict serious side effects more accurately. These models improve early warnings, reduce missed risks, and help regulators act faster. Overall, the research shows how AI can make drug safety monitoring more efficient and reliable, despite challenges like complex and imbalanced data.
Mr. Mohd Azeem,Muhammed Usman Khan,Sufiyan Mehmood Nizami
Flood Hazard Assessment and Spatio-Temporal Analysis in India: Patterns, Causes, Impacts, and Management Perspectives
India is one of the most flood-prone countries in the world, with approximately one-eighth of its total geographical area exposed to recurring flood hazards. Floods in India cause immense loss of human lives, destruction of agricultural land, property, and infrastructure every year. This paper presents a comprehensive spatio-temporal analysis of flood occurrences across India, focusing on the period 2000 to 2019. The study examines the major causes of floods including heavy monsoon rainfall, riverine overflow, glacial lake outburst floods (GLOFs), embankment failures, and rapid urbanisation. Statistical data on flood-affected area, human casualties, crop damage, and economic losses have been compiled and analysed using MS Excel. Three major visualisations — a bar chart of annual flood-affected area, a line graph of human mortality trends, and a pie chart of region-wise flood frequency — have been incorporated to support a clear analytical picture. The study reveals that Bihar, Assam, and Uttar Pradesh are the most consistently flood-affected states. The findings suggest that an integrated flood risk management approach, combining structural measures with non-structural strategies such as early warning systems, land-use planning, and community preparedness, is urgently needed. The paper concludes with policy recommendations and emphasises the role of geospatial technology in flood hazard mapping and mitigation.
Flood hazard, India, Spatio-temporal analysis, Flood-affected area, Flood risk management, Natural disaster, Monsoon, Riverine flooding, Bihar, Assam
A Controlled Observational Study on the Effectiveness of Bombay Hospital's Rehabilitation Protocol for Cervical and Lumbar Nerve Root Compression
The causes of musculoskeletal disability in terms of functional independence and quality of life are largely due to cervical and lumbar nerve root compression that are the major causes of musculoskeletal disability globally. The current controlled observational study was designed to assess the efficacy of Bombay Hospital structured multimodal rehabilitation protocol of pain reduction, functional disability and range of motion in patients with cervical and lumbar nerve root compression. Eighty (80) patients (40 cervical, 40 lumbar) were recruited in the period between January 2021 and December 2022 and divided into the rehabilitation group (n=40) and the control group (n=40), respectively. The outcome measures were Visual Analogue Scale (VAS), Neck Disability Index (NDI), Oswestry Disability Index (ODI), and goniometric range of motion measures at baseline, 6 weeks and 12 weeks. The hypothesis was that the structured rehabilitation plan would have significantly more improvements than a conventional treatment. Findings showed statistically significant decreases in VAS (p<0.001), NDI (p<0.01), and ODI (p<0.001) scores of rehabilitation group over controls. The protocol was found to be clinically better in the restoration of the functional capacity. The research concluded that multimodal rehabilitation protocol at Bombay Hospital is an effective and evidence-based intervention in the management of cervical and lumbar nerve root compression on a conservative basis.
Nerve Root Compression, Rehabilitation Protocol, Cervical Radiculopathy, Lumbar Radiculopathy, Functional Disability
Molecular Design Strategies and QSAR-Based Assessment of Synthesized Heterocycles for Diabetes Management
Diabetes mellitus (DM) represents a critical global metabolic disorder, with an estimated 537 million adults affected worldwide as of 2021. The present study evaluates the molecular design strategies and quantitative structure-activity relationship (QSAR)-based assessment of synthesized heterocyclic scaffolds, including thiazolidinediones, triazoles, oxadiazoles, coumarins, and pyrimidines, as potential antidiabetic agents targeting key enzymes such as α-glucosidase, α-amylase, DPP-IV, and PTP-1B. The objective was to correlate structural descriptors of heterocyclic compounds with their biological activity (IC₅₀) through validated QSAR models and molecular docking studies. A computational methodology integrating 2D/3D-QSAR modeling, pharmacophore mapping, and molecular docking was employed on datasets of synthesized heterocycles reported between 2015 and 2021. The hypothesis posited that electron-withdrawing substituents and hydrogen bond donor/acceptor features on heterocyclic scaffolds significantly enhance antidiabetic potency. Results demonstrated that QSAR models achieved strong statistical validity (R² > 0.85, Q² > 0.60), and docking scores confirmed favorable binding interactions with target proteins. The thiazolidinedione-triazole and coumarin-based hybrids exhibited the most potent IC₅₀ values. In conclusion, QSAR-guided molecular design provides a robust framework for optimizing heterocyclic leads, and future synthesis efforts should prioritize halogen and methoxy substitutions on these scaffolds for enhanced antidiabetic efficacy.
QSAR, Heterocyclic compounds, Molecular docking, Antidiabetic, α-Glucosidase inhibitors
Indigenous Knowledge of Medicinal Plants for Livestock Treatment in Bastar District, Chhattisgarh
The present study investigates the indigenous knowledge of medicinal plants used by tribal communities for livestock treatment in Bastar district of Chhattisgarh, India. The primary objectives were to document ethnoveterinary plant species utilized by tribal healers, identify the plant parts and modes of administration, and assess the prevalence of traditional veterinary knowledge across dominant tribal groups including Gond, Maria, Muria, Bhatra, and Halba. The study adopted a descriptive ethnobotanical research design, employing semi-structured interviews, participatory field observations, and guided field walks among 120 purposively selected informants comprising traditional healers (locally called Guniyas), elderly livestock keepers, and experienced herbal practitioners across seven development blocks of Bastar district. It was hypothesized that the tribal communities of Bastar possess a rich and systematic ethnoveterinary knowledge base linked to the region's biodiversity. Results revealed that 45 plant species belonging to 38 genera and 26 families were used for treating 28 livestock ailments. Leaves (42.2%) were the most frequently used plant part, followed by roots (20.0%) and bark (15.6%). Discussion indicates that this indigenous knowledge system is eroding rapidly due to modernization, deforestation, and generational disconnect. The study concludes that urgent documentation and scientific validation of ethnoveterinary practices in Bastar are essential for biodiversity conservation and sustainable livestock healthcare management.
Ethnoveterinary medicine, Indigenous knowledge, Medicinal plants, Livestock healthcare, Bastar district
AI-Driven Health Support Chatbot Using Retrieval-Augmented Generation (RAG)
The increasing adoption of digital healthcare services has created a strong need for intelligent systems that can deliver dependable and interpretable preliminary medical support. This paper proposes an AI-based healthcare assistance chatbot that integrates machine learning–driven disease prediction with a Retrieval-Augmented Generation (RAG) framework to provide accurate, context-aware, and explainable health guidance. The proposed system allows users to describe their symptoms in natural language, which are analyzed using advanced text processing techniques to extract clinically relevant indicators. A supervised learning–based classification model is employed to predict the most likely disease conditions from the extracted symptoms. To enhance the reliability and safety of the generated responses, a RAG architecture retrieves validated medical knowledge from a structured repository, including disease profiles, common symptoms, severity levels, and recommended precautionary measures. This retrieved information is subsequently combined with a generative language model to produce coherent, evidence-supported, and user-friendly health explanations. By grounding the generative output in verified medical data, the proposed approach significantly reduces hallucination risks and improves transparency in automated health assistance. The system not only supports preliminary symptom assessment but also delivers educational insights and promotes timely consultation with healthcare professionals. The experimental results demonstrate that combining machine learning–based disease prediction with retrieval-driven knowledge grounding substantially improves the accuracy, trustworthiness, and overall quality of digital healthcare support systems.
Predictive Modelling For Lung Cancer Detection Using Machine Learning Techniques
Early and reliable identification of lung cancer plays a critical role in improving patient survival. This paper presents a computer‑aided diagnosis framework for the detection of lung cancer from computed tomography (CT) images using digital image processing and classical machine learning techniques. The proposed pipeline consists of image preprocessing, lesion segmentation, feature extraction and supervised classification. Noise reduction and contrast enhancement are applied during preprocessing, followed by edge‑based and watershed segmentation to isolate candidate regions. Both region‑based and texture‑based features are extracted from the segmented images and are used to train Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Network (ANN) classifiers. Performance is evaluated using accuracy, precision, recall and F1‑score. Experimental results indicate that the ANN classifier achieves the best overall performance for both feature categories. The proposed approach demonstrates that a structured feature‑driven machine learning framework can effectively support automated lung cancer detection from CT images.
Lung cancer detection, CT images, image segmentation, feature extraction, machine learning, ANN, SVM, Random Forest.
Comparative Analysis of Liver Disease Prediction Using Machine Learning Models
Liver disorders represent a major global health challenge and are responsible for a significant number of hospitalizations and long-term complications. Early identification of liver abnormalities is essential for improving treatment outcomes and reducing mortality. However, conventional diagnosis methods rely heavily on laboratory tests and expert interpretation, which are time-consuming and costly. This paper presents a comparative study of machine learning algorithms for predicting liver disease using routinely collected clinical and biochemical parameters. Several supervised learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Gradient Boosting and Neural Network based models, are implemented and evaluated. The Indian Liver Patient Dataset is used as the benchmark dataset. A complete data processing pipeline is developed, consisting of data cleaning, feature transformation, attribute ranking and model evaluation. The performance of the models is analysed using accuracy, precision, recall and F1-score. Experimental results demonstrate that ensemble and neural-based models provide superior predictive performance compared to traditional classifiers. The study confirms that machine learning can serve as a reliable decision-support tool for early liver disease screening.
Liver disease prediction, machine learning, clinical data mining, classification, healthcare analytics.
Accurate dietary monitoring is an essential requirement for preventive healthcare and lifestyle management. However, most existing diet-tracking applications rely heavily on manual food logging, which is time-consuming and often inaccurate. This paper presents an AI-powered food and nutrition analyzer that automatically identifies food items from captured images and generates nutritional information using cloud-based intelligence. The proposed system integrates real-time image acquisition, vision-based food recognition, and nutrition estimation services to provide calorie and macronutrient details to the user. A modular and scalable architecture is adopted to support real-time processing, easy deployment, and future extensibility. Experimental evaluation on common food items demonstrates reliable recognition and practical nutrition estimation suitable for daily usage scenarios.
Food recognition, nutrition estimation, computer vision, cloud AI, diet monitoring.
AI Chatbot for Mental Health Using Natural Language Processing and Safety-Aware Dialogue Management
Mental health disorders such as anxiety, stress, depression and emotional distress are increasing rapidly among students and working professionals. Limited availability of mental health professionals, social stigma and high consultation costs prevent many individuals from seeking timely help. Conversational artificial intelligence offers a scalable and privacy-preserving approach for providing preliminary emotional support and guidance. This paper presents the design and implementation of an AI-based mental health chatbot that uses natural language processing, sentiment and emotion analysis, and safety-aware dialogue management to provide empathetic and context-aware responses. The system detects emotional states such as stress, sadness, anxiety and crisis indicators, and provides supportive responses, self-help strategies and helpline guidance when required. The chatbot is implemented using Python, machine-learning-based intent and risk classifiers, and a lightweight web interface for real-time interaction. Experimental evaluation shows that the proposed system achieves high intent classification accuracy and reliable crisis detection while maintaining low response latency. The proposed solution demonstrates the feasibility of deploying conversational AI as an assistive and scalable mental health support tool.
Mental health chatbot, Natural Language Processing, sentiment analysis, emotion detection, conversational AI, healthcare technology.
Educational Approaches for Strengthening Nurses' Awareness and Acceptance of Artificial Intelligence in Healthcare Delivery: A Hospital-Based Quasi-Experimental Study: Implications for Long-Term Value Creation
The adoption of the use of artificial intelligence (AI) in healthcare requires proper readiness among the nursing leaders. This quasi-experimental study was a hospital-based study that identified the effectiveness of structured educational interventions in improving the level of awareness and acceptance of AI technologies by the nurses in healthcare delivery. India Tertiary care hospital Nurses A sample (n=120) was split into experimental and control groups. The experimental participants were provided with a four-week educational intervention on the basics of AI, its medical uses, and ethics. Validated instruments were used to measure levels of knowledge, attitudes, and acceptance, based on the pre-test and post-test assessments. The findings showed a significant difference in the scores of AI awareness (p<0.001) and acceptance (p<0.001) of the experimental group when compared to control. The intervention led to the improvement of knowledge on the AI use in monitoring patients, aiding diagnosis, and optimizing the workflow. Results indicate that pertinent educational strategies can be used to resolve knowledge gaps and minimize AI adoption resistance. This paper has justifications about introducing systematic training to equip nursing labor with AI-based medical settings and eventually adding value to the long-term with the provision of better quality care and operational efficiency.
Artificial intelligence, nursing education, healthcare technology acceptance, quasi-experimental study, digital health literacy
Radiomics Image Processing and Machine Learning: A Comprehensive Review
Radiomics, which is the high-throughput extraction of quantitative features of medical images, is a paradigm shift in diagnostic and prognostic medicine. Radiomics can be used to find new biomarkers that could be used to define disease phenotypes in a way that human eyes cannot process to locate them by transforming the standard-of-care imaging into data which can be mined. This is a cumulative review that attempts to compile existing literature to analyze the role of integrating sophisticated image processing pipeline and machine learning (ML) or deep learning (DL) algorithms in radiomics. We investigate the general workflow, including image acquisition and segmentation, feature extraction, feature selection, and model development, and both the possibilities provided by it and the technical difficulties. The review uses various applications such as the assessment of pulmonary diseases, oncology, and cardiac risk prediction as an illustration of the state-of-the-art. Threatening issues like feature reproducibility, model interpretability, heterogeneity of data and the need to have a robust validation are addressed in detail. And, last but not the least, we present future directions, which include the necessity of universal protocols, explainable AI (XAI), multimodal data fusion and ethical application of these potent technologies in clinical practices. DOI: https://doi-ds.org/doilink/01.2026-27294922
Radiomics, Machine Learning, Deep Learning, Image Processing, Feature Extraction, Predictive Modeling, Quantitative Imaging, Medical Imaging Analytics.
Assessing Digital Health Literacy As A Determinant Of Self-Management In Chronic Disease Populations
Digital health literacy has emerged as a critical determinant of effective self-management in chronic disease populations. This study examines the relationship between digital health literacy levels and self-management outcomes among individuals with chronic conditions, utilizing the eHealth Literacy Scale as the primary assessment tool. A comprehensive literature review was conducted to synthesize evidence from multiple studies examining digital health literacy across various chronic conditions including diabetes mellitus, hypertension, and cardiovascular diseases. The hypothesis posited that higher digital health literacy scores correlate positively with improved self-management behaviors and health outcomes. Findings revealed that patients with elevated digital health literacy demonstrated significantly enhanced self-management capabilities, better treatment adherence, and improved quality of life. The pooled mean eHealth literacy score across chronic disease populations was notably high, with diabetes and hypertension patients showing particularly elevated scores. However, significant disparities exist based on age, education, socioeconomic status, and digital access. The study concludes that digital health literacy serves as a fundamental prerequisite for effective chronic disease self-management in the digital health era, necessitating targeted interventions to address literacy gaps and health inequalities.
Digital health literacy, chronic disease, self-management, eHealth literacy scale, health outcomes
Impact Of Evidence-Based Nursing On Clinical Outcomes And Safety In Diverse Healthcare Settings: A Multicentric Analysis
Evidence-based practice has emerged as the cornerstone of contemporary nursing, fundamentally transforming healthcare delivery across diverse settings. This multicentric analysis examined the impact of evidence-based nursing interventions on clinical outcomes and patient safety parameters across 288,581 healthcare facilities spanning 32 countries. The study employed a comprehensive methodology integrating systematic reviews, meta-analyses, and multicentric observational data from 2020-2023. Results demonstrated significant improvements in patient safety indicators, including reduction in mortality rates from 7.75% to 6.27%, decrease in healthcare-associated infections by 34.2-49%, reduction in hospital readmissions from 21.5% to 17.8%, and improvement in medication error rates by 30-70%. Evidence-based nursing implementation correlated with shortened hospital stays from 8.5 to 6 days and enhanced patient satisfaction scores by 5-61%. The analysis reveals that evidence-based nursing practices substantially improve clinical outcomes, reduce adverse events, and optimize resource utilization. These findings underscore the critical importance of systematic integration of research evidence into clinical nursing practice for achieving superior patient safety and healthcare quality outcomes across varied institutional settings.
Evidence-based nursing, patient safety, clinical outcomes, healthcare quality, multicentric analysis
Nurse-Led and Physician-Led Interventions in Geriatric Care: A Comparative Health Outcomes Analysis
Geriatric care delivery faces mounting challenges with aging populations requiring specialized interventions to maintain quality of life and functional independence. This comparative analysis examines health outcomes between nurse-led and physician-led interventions in geriatric populations. The study synthesized evidence from randomized controlled trials and observational studies comparing clinical outcomes, cost-effectiveness, and patient satisfaction between these two care delivery models. Data from multiple healthcare settings including primary care, long-term care facilities, and transitional care programs were analyzed. Results demonstrated that nurse practitioners achieved enhanced outcomes in 58% of measured parameters compared to physician-only care, with particular strengths in chronic disease management, medication adherence, and care coordination. Nurse-led interventions reduced 30-day readmission rates by 0.55% and achieved lower mortality rates. Cost analysis revealed significant savings ranging from $924 to $2,626 per patient depending on condition complexity. Both models demonstrated effectiveness, with nurse-led care excelling in accessibility and preventive services while physician-led care showed advantages in acute clinical decision-making. The findings support integrated care models leveraging strengths of both professionals to optimize geriatric health outcomes.
Geriatric care, Nurse-led interventions, Physician-led care, Health outcomes, Cost-effectiveness.
Adolescent health represents a critical public health concern in India, with approximately 253 million adolescents aged 10-19 years constituting the world's largest adolescent population. This study examines the comprehensive status of adolescent health in India, focusing on nutritional deficiencies, mental health challenges, substance abuse, and reproductive health issues. The hypothesis posits that multifactorial determinants including socioeconomic status, educational attainment, and geographical location significantly influence adolescent health outcomes. Utilizing a cross-sectional analytical approach, secondary data from National Family Health Survey-5 (2019-2021) and published literature were systematically reviewed. Results reveal alarming prevalence rates: 59.1% anemia among adolescent girls, 45% undernutrition in girls and 20% in boys, 7.2% adolescent pregnancy, 25-39% depression, 4.5% drug abuse, and 50% of HIV-positive new infections occurring in 10-25 years age group. Statistical analysis demonstrates significant associations between adverse health outcomes and rural residence, low socioeconomic status, and limited education. Discussion emphasizes urgent need for strengthened policy interventions including enhanced implementation of Rashtriya Kishor Swasthya Karyakram and adolescent-friendly health services. The study concludes that comprehensive, multi-sectoral approaches addressing nutritional, mental, reproductive, and behavioral health are essential for improving adolescent well-being and ensuring India's demographic dividend. DOI: https://doi-ds.org/doilink/01.2026-62318779
Adolescent health, India, Anemia, Mental health, Substance abuse