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http://dx.doi.org/10.4103/ijd.ijd_354_22 | DOI Listing |
Talanta
September 2025
Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address:
Food spoilage poses a global challenge with far-reaching consequences for public health, economic stability, and environmental sustainability. Conventional analytical methods for spoilage detection though accurate are often cost-prohibitive, labor-intensive, and unsuitable for real-time or field-based monitoring. Microfluidic paper-based analytical devices (μPADs) have emerged as a transformative technology offering rapid, portable, and cost-effective solutions for food quality assessment.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
JMIR Cancer
September 2025
Department of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.
Background: Disparities in cancer burden between transgender and cisgender individuals remain an underexplored area of research.
Objective: This study aimed to examine the cumulative incidence and associated risk factors for cancer and precancerous conditions among transgender individuals compared with matched cisgender individuals.
Methods: We conducted a retrospective cohort study using patient-level electronic health record (EHR) data from the University of Florida Health Integrated Data Repository between 2012 and 2023.
JMIR Hum Factors
September 2025
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
Background: The rapid advancement of next-generation sequencing has significantly expanded the landscape of precision medicine. However, health care professionals face increasing challenges in keeping pace with the growing body of oncological knowledge and integrating it effectively into clinical workflows. Precision oncology decision support (PODS) tools aim to assist clinicians in navigating this complexity, yet their current functionalities only partially address clinical needs.
View Article and Find Full Text PDF