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Advances in translational science require innovative solutions, and engagement of productive transdisciplinary teams play a critical role. While various forms of scientific meetings have long provided venues for sharing scientific findings and generating new collaborations, many conferences lack opportunities for active discussions. We describe the use of an Un-Meeting to foster innovative translational science teams through engaged discussions across multidisciplinary groups addressing a shared theme. The Un-Meeting was delivered by the University of Rochester Center for Leading Innovation and Collaboration, the national coordinating center for the National Institutes of Health Clinical and Translational Science Awards (CTSA) program. This pilot CTSA program Un-Meeting focused on engaging translational scientists, policy-makers, community members, advocates, and public health professionals to address the opioid crisis. The participant-driven format leveraged lightning talks, attendee-led idea generation, and extensive breakout discussions to foster multidisciplinary networking. Results indicated participation by a broad set of attendees and a high level of networking during the meeting. These results, coupled with the growth of the Un-Meeting across the CTSA Consortium, provide practices and models to potentially advance team and translational science. While future work will further assess the impact of Un-Meetings, this format presents a promising approach to enhance translational science.
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http://dx.doi.org/10.1017/cts.2023.576 | DOI Listing |
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 PDFAm J Physiol Cell Physiol
September 2025
Institute of Pharmacology and Toxicology, Goethe University Frankfurt, Frankfurt, Germany.
The A20 binding inhibitor of nuclear factor-kappa B (NF-κB)-1 (ABIN-1) serves as a ubiquitin sensor and autophagy receptor, crucial for modulating inflammation and cell death. Our previous in vitro investigation identified the LC3-interacting region (LIR) motifs 1 and 2 of ABIN-1 as key mitophagy regulators. This study aimed to explore the in vivo biological significance of ABIN1-LIR domains using a novel CRISPR-engineered ABIN1-ΔLIR1/2 mouse model, which lacks both LIR motifs.
View Article and Find Full Text PDFPLoS One
September 2025
CIRAD, UMR ASTRE, Montpellier, France.
Since the 2013-2014 Ebola virus disease outbreak, Guinea has faced recurrent epidemics of viral hemorrhagic fevers. Although the country has learned from these epidemics by improving its disease surveillance and investigation capacities, local authorities and stakeholders, including community actors, are not sufficiently involved in the disease-emergence response. As a result, measures are not fully understood and have failed to engage local stakeholders.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Nephrology, Chungnam National University, Daejeon, Republic of Korea.
Diabetic kidney disease (DKD) involves oxidative stress-driven damage to glomeruli (Gloms) and proximal convoluted tubules (PCT). NAD(P)H: quinone oxidoreductase 1 (NQO1) regulates redox balance, but its compartment-specific role remains unclear. Streptozotocin (STZ)-induced hyperglycemia increased albuminuria and foot process effacement, with NQO1 KO (NKO) mice exhibiting greater podocyte injury than WT, indicating exacerbated glomerular damage.
View Article and Find Full Text PDFJ Hepatol
July 2025
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany; Medical Oncology, National Center for Tumor Disease
Artificial intelligence (AI) methods in hepatology have proliferated since the mid-2010s, with numerous publications and some regulatory approvals. Yet, adoption of AI methods in real-world clinical practice and clinical research remains limited. Despite clear benefits of using AI to analyze complex data types in hepatology, such as histopathology, radiology images, multi-omics and more recently, natural language patient data, there are still substantial barriers and challenges to its integration into routine clinical workflows.
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