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http://dx.doi.org/10.1098/rsif.2018.0816 | DOI Listing |
Front Microbiol
January 2025
Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
[This corrects the article DOI: 10.3389/fmicb.2024.
View Article and Find Full Text PDFGenes Chromosomes Cancer
January 2025
School of Geography and the Environment, University of Oxford, UK.
Given the high lethality of cancer, identifying its risk factors is crucial in both epidemiology and cancer research. This study employs a novel bibliometric analysis method, which uses the tidytext package and tidy tools in R. This approach surpasses traditional tools like VOSviewer, offering more comprehensive and complex keyword data and clearer results compared to Bibliometrix.
View Article and Find Full Text PDFNeuropsychol Dev Cogn B Aging Neuropsychol Cogn
January 2025
Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
Greater neighborhood disadvantage is associated with poorer global cognition. However, less is known about the variation in the magnitude of neighborhood effects across individual cognitive domains and whether the strength of these associations differs by individual-level factors. The current study investigated these questions in a community sample of older adults ( = 166, mean age = 72.
View Article and Find Full Text PDFIndian J Thorac Cardiovasc Surg
February 2025
Dept of CTVS, NEIGRIHMS, Shillong, India.
Isolated right superior vena cava (RSVC) drainage into the left atrium (LA) is a rare congenital anomaly, presenting diagnostic and management challenges. This study presents two cases of isolated RSVC drainage into the LA alongside a comprehensive literature review to improve understanding and delineate optimal surgical approaches. The study describes two cases of isolated RSVC drainage into the LA and their surgical management.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks).
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