Unveiling the interplay of medical professionalism, mental well-being and coping in medical students: a qualitative phenomenological study.

Background: Medical students face significant stress and challenges that impact their professional development by affecting their levels of medical professionalism (MP), coping ability, and mental well-being (MWB). Given the high-stakes environment of medical education, understanding the interplay between these factors is crucial. This study aims to explore undergraduate medical students' lived experiences of MP, coping strategies (CSs), and MWB to inform the development of effective support systems.

Design of composite chitosan/algae/zeolite by freeze- or air-drying: A comparative adsorbent analysis for optimized removal of brilliant green dye.

A bio-composite material was developed that contains chitosan, food-grade algae, and zeolite for the removal of brilliant green (BG) dye. The synthesized bio-composite was dried via two different methods (air-drying; AD, and freeze-drying; FD). The physicochemical characterization of air-dried chitosan-algae-zeolite (Cs-Alg-Zl-AD) and freeze-dried chitosan-algae-zeolite (Cs-Alg-Zl-FD) were investigated by spectroscopy (FTIR, SEM-EDX, and XPS), diffraction (XRD), surface charge via pH, specific surface area (SSA) and elemental analyses.

Decoding the interplay of medical professionalism, mental well-being, and coping in undergraduate medical students across culture: using structural equation modeling.

Introduction: The rigorous nature of medical education, long and night shifts, and prevalent issues like stress, anxiety, and depression affect medical students' mental well-being and medical professionalism. This study aims to explore the intricate relationships between mental well-being, medical professionalism, and coping strategies, among undergraduate medical students, utilizing structural equation modeling (SEM) to unravel these dynamics.

Methods: Conducted at Universiti Sains Malaysia, this cross-sectional study involved 234 medical students from the 1st, 3rd, and 5th years of the MBBS program.

Repeated time-series cross-validation: A new method to improved COVID-19 forecast accuracy in Malaysia.

Forecasting COVID-19 cases is challenging, and inaccurate forecast values will lead to poor decision-making by the authorities. Conversely, accurate forecasts aid Malaysian government authorities and agencies (National Security Council, Ministry of Health, Ministry of Finance, Ministry of Education, and Ministry of International Trade and Industry) and financial institutions in formulating action plans, regulations, and legal acts to control COVID-19 spread in the country. Therefore, this study proposes Repeated Time-Series Cross-Validation, a new data-splitting strategy to identify the best forecasting model that is capable of producing the lowest error measures value and a high percentage of forecast accuracy for COVID-19 prediction in Malaysia.

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources.