AI Assissted Telemedicine Kiosk for Rural Healthcare Transformation
Design and Evaluation of Paediatric Health Prediction Systems for Low-Resource Settings
CNN-Based System for Enhanced Tuberculosis Diagnosis using Chest X-Rays
“Beyond the Blood”: Revolutionizing Leukemia Treatment Through Car T-Cell Therapy: Advances, Challenges, and Future Horizons
Charting the Molecular Landscape of Chordoma: Bridging Molecular Insights with Novel Drug Modalities
Raising Concern of Substance Abuse among Adolescents in India: A Narrative Review
Aquagenic Urticaria: When Water Becomes Lethal
Transferosomes an Emerging Versatile Transformation in Research: An Advanced Review
Executing Quality Management Systems in Laboratory Testing and Biomedical Product Control
Rubinstein - Taybi Syndrome: A Rare Genetic Disorder
Charting the Molecular Landscape of Chordoma: Bridging Molecular Insights with Novel Drug Modalities
AI Assissted Telemedicine Kiosk for Rural Healthcare Transformation
Raising Concern of Substance Abuse among Adolescents in India: A Narrative Review
CNN-Based System for Enhanced Tuberculosis Diagnosis using Chest X-Rays
Optimizing Patient Safety and Dose Reduction Strategies in Abdominal, Chest, and Skull CT Imaging: A Comprehensive Analysis of Effective Dose Quantification
Healthcare accessibility in rural India is often hindered by limited infrastructure, lack of timely diagnosis, and unavailability of essential medicines, leading to preventable health risks. To address this, we propose an AI assisted kiosk that integrates symptom analysis with automated medicine dispensing. Users input basic symptoms, which are processed by an AI model trained to detect common illnesses. Based on the analysis, the kiosk recommends and securely dispenses suitable medicines, ensuring timely treatment without relying on immediate medical staff. Designed to be cost-effective, portable, and user-friendly, the system provides round-the-clock access to essential care. This project demonstrates a scalable solution to bridge healthcare gaps and improve medical support for underserved rural communities.
This paper presents the design and implementation of EarlyAid, a mobile-based Paediatric Health Prediction System tailored for low-resource settings. The proposed approach combines a hybrid AI algorithm that integrates rule-based symptom mapping with a lightweight TensorFlow Lite classifier to generate condition probabilities based on caregiver-reported symptoms. The system operates entirely offline, supports multilingual input (English, Tonga, Bemba, Nyanja), and uses age-specific visual prompts to improve usability. Unlike conventional mHealth tools that rely on cloud infrastructure and generic symptom checkers, EarlyAid is optimized for Zambian households with limited connectivity and diverse literacy levels. The novelty of this work lies in its privacy-preserving, offline-first architecture and its locally adapted symptom-to-condition mapping, which enables caregivers to receive predictive health insights without clinical supervision. Testing results show an average prediction accuracy of 89.3%, with strong caregiver feedback on cultural relevance and ease of use. EarlyAid demonstrates a scalable model for intelligent paediatric health support in underserved communities.
Tuberculosis (TB) remains a serious global health problem, especially in regions with limited access to expert medical care. While chest X-rays are widely used for TB screening, interpreting them accurately can be challenging. This work introduces an automated system that helps detect TB from X-ray images using advanced image processing and artificial intelligence. The system first enhances and isolates the lung areas using the nnU-Net model, then analyzes them with a Swin Transformer to identify signs of infection. Tests on well-known datasets, such as Shenzhen and Montgomery County, showed excellent performance, achieving 95.2% accuracy and a Dice score of 0.94. Overall, this approach offers a reliable and scalable tool that could support faster and more consistent TB diagnosis, particularly in resource-limited healthcare settings.
Chimeric antigen receptor (CAR) T-cell therapy has emerged as a groundbreaking treatment modality in the management of hematologic malignancies, particularly leukemia. By genetically engineering autologous T cells to recognize tumor-associated antigens, CAR T-cell therapy enables precise targeting and potent cytotoxic responses against malignant cells. Clinical trials and real-world studies have demonstrated remarkable remission rates in relapsed and refractory B-cell acute lymphoblastic leukemia (B-ALL), leading to regulatory approvals and the integration of CAR T-cell therapy into clinical practice. Despite these advances, significant challenges remain, including therapy-associated toxicities such as cytokine release syndrome and neurotoxicity, antigen escape, limited persistence of CAR T cells, and barriers to accessibility and scalability. Ongoing research is focused on optimizing CAR design, improving safety profiles,expanding applications to other leukemia subtypes, and developing combination strategies to enhance durability of response. This review provides a comprehensive overview of the current landscape of CAR T-cell therapy in leukemia, highlighting clinical outcomes, mechanistic insights, limitations, and future directions in this rapidly evolving field.
Chordoma is a rare, slow-growing malignant tumor arising from embryonic notochordal remnants, most commonly affecting the skull base, mobile spine, and sacrococcygeal regions. Despite its low incidence, chordoma presents major therapeutic challenges due to its strong propensity for local recurrence, resistance to conventional chemotherapy, and limited systemic treatment options..This review explores the molecular and genetic mechanisms underlying chordomagenesis, emphasizing the central role of brachyury (TBXT) overexpression, chromosomal instability, and deregulation of key oncogenic pathways including RTK/PI3K/AKT/mTOR and MAPK/ERK. Additional mechanisms such as CDKN2A/PTEN loss, JAK/STAT activation, SOX9-mediated stemness, and epigenetic dysregulation contribute to tumor progression and therapeutic resistance and further summarize current molecularly targeted therapies such as PDGFR, EGFR, and VEGFR inhibitors, mTOR modulators, and CDK4/6 inhibitors highlighting clinical outcomes and ongoing limitations. Collectively, emerging molecular insights support a precision oncology framework integrating targeted drug combinations, immunotherapies, and biomarker-driven strategies to improve progression-free survival and redefine the clinical management of chordoma.
