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Assessing Traumatic Brain Injury in Refugees: Feasibility, Usability, and Prevalence Insights From a US-Based Clinical Sample.
J Head Trauma Rehabil
January 2025
Author Affiliations: Boston University School of Public Health, Boston, Massachusetts (Ms Sherman Rosa); Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts (Mr Nadal); and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (Dr Saadi).
Objective: This study assessed (1) the feasibility and usability of traumatic brain injury (TBI) assessment using the Ohio State University TBI Identification Method (OSU-TBI-ID) in a sample of English and Spanish-speaking refugees and asylum seekers (hereafter refugees), and (2) the prevalence and characteristics of TBI in this population.
Setting And Participants: Refugees seeking care from Massachusetts General Hospital (MGH) Asylum Clinic, the MGH Chelsea HealthCare Center, and other asylum programs in the Greater Boston Area.
Design And Main Measures: Bilingual clinical research coordinators screened 158 English and Spanish-speaking refugees using the OSU-TBI-ID.
The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population-Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis.
JMIR Res Protoc
January 2025
South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.
Background: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing.
View Article and Find Full Text PDFHow likely is it that a virus or bacteria is causing a patient's symptoms? A new approach to interpret the outcome from multi-pathogen PCR.
Infect Dis (Lond)
January 2025
Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA.
Background: Whether a detected virus or bacteria is a pathogen that may require treatment, or is merely a commensal 'passenger', remains confusing for many infections. This confusion is likely to increase with the wider use of multi-pathogen PCR.
Objectives: To propose a new statistical procedure to analyse and present data from case-control studies clarifying the probability of causality.
Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.
Sci Rep
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
View Article and Find Full Text PDFTime-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm.
Sensors (Basel)
January 2025
Satellite Application Division, Korea Aerospace Research Institute (KARI), Daejeon 34133, Republic of Korea.
For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations.
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