We designed and evaluated an innovative computer-aided-learning environment based on the on-line integration of computer controlled medical diagnostic devices and a medical information system for use in the preclinical medical physics education of medical students. Our learning system simulates the actual clinical environment in a hospital or primary care unit. It uses a commercial medical information system for on-line storage and processing of clinical type data acquired during physics laboratory classes. Every student adopts two roles, the role of 'patient' and the role of 'physician'. As a 'physician' the student operates the medical devices to clinically assess 'patient' colleagues and records all results in an electronic 'patient' record. We also introduced an innovative approach to the use of supportive education materials, based on the methods of adaptive e-learning. A survey of student feedback is included and statistically evaluated. The results from the student feedback confirm the positive response of the latter to this novel implementation of medical physics and informatics in preclinical education. This approach not only significantly improves learning of medical physics and informatics skills but has the added advantage that it facilitates students' transition from preclinical to clinical subjects.
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http://dx.doi.org/10.1016/j.ejmp.2011.12.002 | DOI Listing |
Nat Commun
January 2025
School of Physics, Beihang University, Haidian District, Beijing, China.
Topology is being widely adopted to understand and to categorize quantum matter in modern physics. The nexus of topology orders, which engenders distinct quantum phases with benefits to both fundamental research and practical applications for future quantum devices, can be driven by topological phase transition through modulating intrinsic or extrinsic ordering parameters. The conjoined topology, however, is still elusive in experiments due to the lack of suitable material platforms.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, C11, 75185, Uppsala, Sweden.
The existence of transmissible amyloid fibril strains has long intrigued the scientific community. The strain theory originates from prion disorders, but here, we provide evidence of strains in systemic amyloidosis. Human AA amyloidosis manifests as two distinct clinical phenotypes called common AA and vascular AA.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.
View Article and Find Full Text PDFJ Clin Monit Comput
January 2025
Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AZ, Eindhoven, the Netherlands.
Unobtrusive pulse rate monitoring by continuous video recording, based on remote photoplethysmography (rPPG), might enable early detection of perioperative arrhythmias in general ward patients. However, the accuracy of an rPPG-based machine learning model to monitor the pulse rate during sinus rhythm and arrhythmias is unknown. We conducted a prospective, observational diagnostic study in a cohort with a high prevalence of arrhythmias (patients undergoing elective electrical cardioversion).
View Article and Find Full Text PDFNat Commun
January 2025
Laboratory of Molecular Neurobiology, Graduate School of Biostudies, Kyoto University, Sakyo-ku, Kyoto, Japan.
Commensal bacteria affect host health by producing various metabolites from dietary carbohydrates via bacterial glycometabolism; however, the underlying mechanism of action remains unclear. Here, we identified Streptococcus salivarius as a unique anti-obesity commensal bacterium. We found that S.
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