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To what extent are digital health interventions targeting HIV care cascade among mobile populations feasible, acceptable, and effective? A mixed methods systematic review protocol.
Syst Rev
January 2025
Division of Epidemiology & Biostatistics, School of Public Health, University of Cape Town, Cape Town, South Africa.
Introduction: Human mobility is associated with an increased risk of HIV acquisition and disengagement from HIV care, leading to poorer health outcomes among highly mobile individuals compared to less mobile individuals. Mobile individuals, broadly defined as those who temporally, seasonally, or permanently move from one place to another for voluntary or involuntary reasons, face many challenges in accessing HIV care services. These challenges include logistical difficulties, interruptions in HIV care continuity, and limited access to services across different locations, which together hinder timely testing, treatment initiation, and viral suppression.
View Article and Find Full Text PDFSocial determinants of student mental health before and after the Beirut port explosion: two cross-sectional studies.
Confl Health
January 2025
Trinity Centre for Global Health, Trinity College Dublin, Dublin, Ireland.
Background: Students in Lebanon are facing the devastating impact of multiple national crises, including an unprecedented economic collapse and the Beirut port explosion that killed hundreds, injured thousands, and displaced hundreds of thousands of people. The aim of this study was to identify key social determinants of common mental health symptoms before and after the Beirut port explosion for students at the American University of Beirut, a university based around 4 km from the port.
Methods: Two cross-sectional studies were conducted using a representative sample of undergraduate and graduate students at the American University of Beirut.
Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples.
Sci Rep
January 2025
School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. Still, when the training samples for each class are limited, it will not only face the problem of overfitting but also significantly affect the classification result.
View Article and Find Full Text PDFTwo-dimensional Monte Carlo simulation coupled with multilinear regression modeling of source-specific health risks from groundwater.
J Hazard Mater
January 2025
University of Belgrade, Faculty of Technology and Metallurgy, Karnegijeva 4, Belgrade 11120, Serbia. Electronic address:
Effective protection of groundwater requires an accurate health risk assessment of contaminants; however, the diversity of pollution sources, variability, and uncertainties in exposure parameters present significant challenges in this assessment. In this study, groundwater risk estimates associated with NO, and F, along with fourteen heavy metal(loid)s (V, Cr, Mn, Fe, Ni, Cu, As, Co, Cd, Se, Pb, Hg, Zn, and Al) in an agricultural area were optimized by implementing positive matrix factorization (PMF), multilinear regression, and two-dimensional Monte Carlo simulations to characterize source-specific health risks. Groundwater pollution was analyzed considering regional variations, including differences in elevation, land use and land cover, and soil types.
View Article and Find Full Text PDFDeep learning aided determination of the optimal number of detectors for photoacoustic tomography.
Biomed Phys Eng Express
January 2025
Applied Sciences, Indian Institute of Information Technology Allahabad, Deoghat, Jhalwa, Allahabad, 211012, INDIA.
Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology suffers from significant streak artifacts and low-quality images. The integration of deep learning (DL), specifically convolutional neural networks (CNNs), has recently demonstrated powerful performance in various fields of PAT.
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