Interprofessional education for health care professionals should be anchored at all training and study locations across Germany. In cooperation with the Medical Faculty Mannheim, an education concept trialed there, namely a longitudinal interprofessional learning sequence, was transferred and adapted to the Medical Faculty Dresden as part of the "Operation Team" support program. Here, the structured analysis and evaluation of the knowledge transfer experience is presented from the perspective of the transferee. From these findings, recommendations are derived for the planning of knowledge transfer projects. The consulting work between the two faculties was listed chronologically including knowledge transfer outcomes and was described and analyzed using the comparative categories identified in sociological systems theory and in the knowledge transfer literature. In addition, knowledge transfer outcomes were categorized according to their use and their relevance to the progress of the project was assessed. The coordination teams initiated 13 consulting sessions, primarily held virtually or by telephone. From these, 36 knowledge transfer outcomes were identified, of which most were of high relevance for the transferee in all use categories. The knowledge transfer core themes were of a strategic (e.g. the consolidation of interprofessional teaching) and content-based/didactic-methodological nature (e.g. interprofessional session design, tutor training). The consulting sessions played a major role in facilitating the establishment of two interprofessional learning sequences and the piloting of the associated sessions at the Dresden site. The recommendations derived for a successful transfer could also be of help for other transfer projects.
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http://dx.doi.org/10.3205/zma001529 | DOI Listing |
Brief Bioinform
November 2024
Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea.
Identifying new compounds that interact with a target is a crucial time-limiting step in the initial phases of drug discovery. Compound-protein complex structure-based affinity prediction models can expedite this process; however, their dependence on high-quality three-dimensional (3D) complex structures limits their practical application. Prediction models that do not require 3D complex structures for binding-affinity estimation offer a theoretically attractive alternative; however, accurately predicting affinity without interaction information presents significant challenges.
View Article and Find Full Text PDFiScience
January 2025
University of Groningen - GELIFES, Groningen, the Netherlands.
The One Health approach musters growing concerns about antimicrobial resistance due to the increased use of antibiotics in healthcare and agriculture, with all of its consequences for human, livestock, and environmental health. In this perspective, we explore the current knowledge on how interactions at different levels of biological organization, from genetic to ecological interactions, affect the evolution of antimicrobial resistance. We discuss their role in different contexts, from natural systems with weak selection, to human-influenced environments that impose a strong pressure toward antimicrobial resistance evolution.
View Article and Find Full Text PDFIdentifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual tuning and system-specific knowledge. Here, we introduce geom2vec, in which pretrained graph neural networks (GNNs) are used as universal geometric featurizers. By pretraining equivariant GNNs on a large dataset of molecular conformations with a self-supervised denoising objective, we obtain transferable structural representations that are useful for learning conformational dynamics without further fine-tuning.
View Article and Find Full Text PDFOpen Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
Biochimie
January 2025
LAQV, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal. Electronic address:
This study focuses on the quaternary structure of the viper-secreted phospholipase A (PLA), a central toxin in viper envenomation. PLA enzymes catalyse the hydrolysis of the sn-2 ester bond of membrane phospholipids. Small-molecule inhibitors that act as snakebite antidotes, such as varespladib, are currently in clinical trials.
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