Development of a rapid and highly sensitive nucleic acid-based diagnostic test for schistosomes, leveraging on identical multi-repeat sequences.

Introduction: Schistosomiasis (Bilharzia), a neglected tropical disease caused by parasites, afflicts over 240 million people globally, disproportionately impacting Sub-Saharan Africa. Current diagnostic tests, despite their utility, suffer from limitations like low sensitivity. Polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR) remain the most common and sensitive nucleic acid amplification tests.

Targeting InhA in drug-resistant Mycobacterium tuberculosis: potent antimycobacterial activity of diaryl ether dehydrozingerone derivatives.

Tuberculosis (TB) remains a major global threat, with 10 million new cases and 1.5 million deaths each year. In multidrug-resistant tuberculosis (MDR-TB), resistance is most commonly observed against isoniazid (INH) and rifampicin (RIF), the two frontline drugs.

Mental health for the indigenous population in Chhattisgarh, India: Navigating challenges and crafting solutions.

Introduction: The ST population, residing in isolated, underdeveloped areas, faces significant health disparities compared to non-tribal communities. In particular, the lack of mental health infrastructure in these regions exacerbates their health challenges. Tribal communities possess distinct cultural beliefs surrounding health and illness, yet scant information exists regarding their physical and mental well-being.

Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning.

Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.