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Deep learning for kidney trauma detection: CT image algorithm performance and external validation: experimental study.
Int J Surg
January 2025
Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.
Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.
View Article and Find Full Text PDFEfficient visual word recognition presumably relies on orthographic prediction error (oPE) representations. On the basis of a transparent neurocognitive computational model rooted in the principles of the predictive coding framework, we postulated that readers optimize their percept by removing redundant visual signals, allowing them to focus on the informative aspects of the sensory input (i.e.
View Article and Find Full Text PDFAutomated High-Throughput Affinity Capture-Mass Spectrometry Platform with Data-Independent Acquisition.
J Proteome Res
January 2025
Discovery Research, AbbVie, Inc., 1 North Waukegan Rd., North Chicago, Illinois 60064, United States.
Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis.
View Article and Find Full Text PDFQuantifying Competitive Fitness in Yeast with High-Throughput Fluorescence Microscopy Imaging.
Curr Protoc
January 2025
Department of Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada.
Competitive fitness is a fundamental concept in evolutionary biology that captures the ability of organisms to survive, reproduce, and compete for resources in their environment. Competitive fitness is typically assessed in the lab by growing two or more competitors together and measuring the frequency of each at multiple time points. Traditional microbial competitive fitness assays are labor intensive and involve plating on solid medium and counting colonies.
View Article and Find Full Text PDFConcordance Between Asthma Symptom Reports and Objective Lung Function, and Associations With Sleep Outcomes in Urban Children.
Pediatr Pulmonol
January 2025
Department of Pediatrics, Hasbro Children's Hospital, Providence, Rhode Island, USA.
Objectives: To examine the extent to which asthma symptom concordance (ASC) or discordance (ASD) is associated with sleep outcomes in children with persistent asthma. Also, to investigate whether the association between ASC and sleep outcomes varies as a function of children's level of asthma control and severity.
Methods: A retrospective data analysis of Project NAPS (Nocturnal Asthma and Performance in School), an observational study which examined asthma and sleep outcomes in children with persistent asthma.
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