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VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).
Microsc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFBidirectional relationships between phonological processing and basic number knowledge in kindergarten children: a longitudinal study.
BMC Psychol
January 2025
Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Beijing Normal University, Beijing, 100875, China.
An ongoing debate on the association between phonological processing and number knowledge concerns the extent to which they influence each other during early childhood. The current study aims to establish the direction of the developmental relationship between these two kinds of abilities at an early age. Eighty-two Chinese kindergarten children were followed from 5 to 6 years old with a one-year interval.
View Article and Find Full Text PDFEnhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems.
BMC Med Inform Decis Mak
January 2025
School of Medicine, University of Colorado, Aurora, CO, USA.
Background: In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues.
View Article and Find Full Text PDFExplainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.
Nat Microbiol
January 2025
School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China.
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptides (AMPs) and virtually modifies peptide sequences to produce more potent AMPs, akin to in silico directed evolution. We applied this model to peptides encoded in low-abundance human oral bacteria, resulting in the virtual evolution of 32 peptides into potent AMPs.
View Article and Find Full Text PDFAutomating the amino acid identification in elliptical dichroism spectrometer with Machine Learning.
PLoS One
January 2025
Electrical and Computer Engineering, University of Denver, Denver, Colorado, United States of America.
Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer.
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