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Machine Learning Models for the Early Real-Time Prediction of Deterioration in Intensive Care Units-A Novel Approach to the Early Identification of High-Risk Patients.
J Clin Med
January 2025
Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, 22083 Berlin, Germany.
Predictive machine learning models have made use of a variety of scoring systems to identify clinical deterioration in ICU patients. However, most of these scores include variables that are dependent on medical staff examining the patient. We present the development of a real-time prediction model using clinical variables that are digital and automatically generated for the early detection of patients at risk of deterioration.
View Article and Find Full Text PDFUnrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data.
Med Biol Eng Comput
January 2025
Department of Electrical and Communication Engineering, United Arab Emirates University, Asharej, Al Ain, 15551, Abu Dhabi, United Arab Emirates.
Photoacoustic tomography (PAT) has emerged as a promising imaging modality for breast cancer detection, offering unique advantages in visualizing tissue composition without ionizing radiation. However, limited-view scenarios in clinical settings present significant challenges for image reconstruction quality and computational efficiency. This paper introduces novel unrolled deep learning networks based on split Bregman total variation (SBTV) and relaxed basis pursuit alternating direction method of multipliers (rBP-ADMM) algorithms to address these challenges.
View Article and Find Full Text PDFInvestigating the moments of "aha" and "hmm" through acoustic analysis of voice and speech in pre-service physics teacher education-A novel method for identifying significant learning moments.
PLoS One
January 2025
Department of Teacher Education, University of Jyväskylä, Jyväskylä, Finland.
The aim of the study was to find whether certain meaningful moments in the learning process are noticeable through features of voice and how acoustic voice analyses can be utilized in learning research. The material consisted of recordings of nine university students as they were completing tasks concerning direct electric circuits as part of their course of teacher education in physics. Prosodic features of voice-fundamental frequency (F0), sound pressure level (SPL), acoustic voice quality measured by LTAS, and pausing-were investigated.
View Article and Find Full Text PDFUnveiling Paramyrothecium kamalii (Stachybotryaceae) as a novel foliar fungal pathogen on Matourea azurea in Kerala, India, based on morpho-cultural, pathological and molecular phylogenetic evidences.
Arch Microbiol
January 2025
Andaman and Nicobar Regional Centre, Botanical Survey of India, Haddo, Port Blair, 744102, India.
During recent survey for the investigation of foliar fungi in Kerala, India, a new species of foliicolous hyphomycete, Paramyrothecium kamalii was discovered on living leaves of Matourea azurea (Plantaginaceae) based on morpho-cultural characteristics and multigene (ITS, LSU, cmdA, tub2, and rpb2) phylogenetic analysis; is described, illustrated and discussed. In vitro Pathogenicity tests were performed and confirmed the pathogenic nature of the fungus, thereby fulfilling Koch's postulates. Phylogenetically, P.
View Article and Find Full Text PDFCentral nervous system involvement in Waldenström macroglobulinemia: a comparative population-based study of Bing-Neel syndrome and histological transformation.
Ann Hematol
January 2025
Department of Pathology, Copenhagen University Hospital - Zealand University Hospital Roskilde, Roskilde, Denmark.
Central nervous system (CNS) involvement in Waldenström macroglobulinemia (WM) is a rare complication that can manifest as Bing-Neel syndrome (BNS) or as histological transformation (HT) to diffuse large B-cell lymphoma (DLBCL). We report data from a single-center cohort of 469 patients consecutively diagnosed with WM between 2000 and 2022. BNS was identified in 1.
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