Design and Evaluation of Pediatric Health Prediction Systems for Low-Resource Settings
CNN-Based System for Enhanced Tuberculosis Diagnosis using Chest X-Rays
AI-Assisted Tele-Medicine Kiosk for Rural Health Care Transformation
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-Assisted Tele-Medicine Kiosk for Rural Health Care 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
This paper presents the design and implementation of EarlyAid, a mobile-based pediatric 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, and 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 pediatric 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.
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 work demonstrates a scalable solution to bridge healthcare gaps and improve medical support for underserved rural communities.
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 malignant bone tumor arising from persistent notochordal remnants and is characterized by slow growth, local aggressiveness, and a high rate of recurrence. Surgical resection and radiotherapy remain the mainstays of treatment; however, complete excision is frequently challenging due to the tumor's proximity to critical neurovascular structures, and conventional chemotherapy has shown limited efficacy. Advances in molecular biology have significantly improved understanding of chordoma pathogenesis, revealing key oncogenic drivers such as brachyury (TBXT), receptor tyrosine kinase activation, PI3K/AKT/mTOR signaling, and cell-cycle dysregulation involving CDKN2A and PTEN loss. This review comprehensively summarizes the molecular pathophysiology of chordoma, including genetic, epigenetic, and signaling pathway alterations, and discusses their clinical implications. Emerging molecular-targeted therapies, including tyrosine kinase inhibitors, mTOR inhibitors, CDK4/6 inhibitors, brachyury-targeted approaches, and novel combination strategies, are critically evaluated. Understanding these molecular mechanisms provides a foundation for precision-based therapeutic approaches and highlights future directions aimed at improving disease control, survival outcomes, and quality of life for patients with chordoma.
