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Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal.
Brain Sci
January 2025
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA.
Objective: Functional magnetic resonance imaging data pose significant challenges due to their inherently noisy and complex nature, making traditional statistical models less effective in capturing predictive features. While deep learning models offer superior performance through their non-linear capabilities, they often lack transparency, reducing trust in their predictions. This study introduces the Time Reversal (TR) pretraining method to address these challenges.
View Article and Find Full Text PDFTargeting LMO2-induced autocrine FLT3 signaling to overcome chemoresistance in early T-cell precursor acute lymphoblastic leukemia.
Leukemia
January 2025
Australian Centre for Blood Diseases (ACBD), School of Translational Medicine, Monash University, Melbourne, VIC, Australia.
Early T-cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) is an immature subtype of T-cell acute lymphoblastic leukemia (T-ALL) commonly show deregulation of the LMO2-LYL1 stem cell transcription factors, activating mutations of cytokine receptor signaling, and poor early response to intensive chemotherapy. Previously, studies of the Lmo2 transgenic mouse model of ETP-ALL identified a population of stem-like T-cell progenitors with long-term self-renewal capacity and intrinsic chemotherapy resistance linked to cellular quiescence. Here, analyses of Lmo2 transgenic mice, patient-derived xenografts, and single-cell RNA-sequencing data from primary ETP-ALL identified a rare subpopulation of leukemic stem cells expressing high levels of the cytokine receptor FLT3.
View Article and Find Full Text PDFAssociations between long-term exposure to fine particulate matter and its constituents with lung cancer incidence: Evidence from a prospective cohort study in Beijing, China.
Environ Pollut
January 2025
College of Environmental Sciences and Engineering, Peking University, Beijing, China; State Environmental Protection Key Laboratory of Atmospheric Exposure and Health Risk Management, Center for Environment and Health, Peking University, Beijing, China.
Association between long-term exposure to ambient fine particulate matter (PM) and lung cancer incidence is well-documented. However, the role of different PM constituents [black carbon (BC), ammonium (NH), nitrate (NO), organic matter (OM), and inorganic sulfate (SO)] remain unclear. The study aimed to specify the associations between PM constituents and lung cancer incidence.
View Article and Find Full Text PDFAI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.
Front Oncol
January 2025
School of Engineering, Newcastle University, Newcastle Upon Tyne, United Kingdom.
Background: Non-muscle-invasive Bladder Cancer (NMIBC) is notorious for its high recurrence rate of 70-80%, imposing a significant human burden and making it one of the costliest cancers to manage. Current prediction tools for NMIBC recurrence rely on scoring systems that often overestimate risk and lack accuracy. Machine learning (ML) and artificial intelligence (AI) are transforming oncological urology by leveraging molecular and clinical data to enhance predictive precision.
View Article and Find Full Text PDFMachine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.
Front Oncol
January 2025
College of Medicine and Health, School of Medical Sciences, Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications.
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