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Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.
Nat Cancer
January 2025
Dept. of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility.
View Article and Find Full Text PDFDesign, synthesis and evaluation of (E)-1-(4-(2-(1H-pyrazol-5-yl)vinyl)phenyl) derivatives as next generation selective RET inhibitors overcoming RET solvent front mutations (G810C/R).
Eur J Med Chem
January 2025
College of Chemistry and Materials Science, Zhejiang Normal University, No. 688 Yingbin Road, Jinhua, Zhejiang Province, 321004, China. Electronic address:
RET is a well-recognized drug target for cancer treatment. Despite the promising efficacy of selective second-generation RET inhibitors Selpercatinib and Pralsetinib, the clinical benefits have been compromised due to the quickly developed resistance to these drugs. RET G810 mutations at the solvent front site have been identified as the major on-target mutations contributing to resistance against Selpercatinib and Pralsetinib.
View Article and Find Full Text PDFThe TOXIN knowledge graph: supporting animal-free risk assessment of cosmetics.
Database (Oxford)
January 2025
Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.
The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.
View Article and Find Full Text PDFInterventions to reduce non-prescription antimicrobial sales in community pharmacies.
Cochrane Database Syst Rev
January 2025
Global Health Nursing, Graduate School of Nursing Science, St. Luke's International University, Chuo-ku, Japan.
Background: Antimicrobial resistance (AMR) is a major global health concern. One of the most important causes of AMR is the excessive and inappropriate use of antimicrobial drugs in healthcare and community settings. Most countries have policies that require antimicrobial drugs to be obtained from a pharmacy by prescription.
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