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Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children-and captured individual differences in later recognition memory-but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed 2 network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in each age group differently. Thus, functional connectivity (FC) predicts SA and WM in youth, with networks predicting WM performance differing more between youths and adults than those predicting SA.
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http://dx.doi.org/10.1371/journal.pbio.3001938 | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815648 | PMC |
Diagn Pathol
September 2025
Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Background: Gastric cancer is one of the most common cancers worldwide, with its prognosis influenced by factors such as tumor clinical stage, histological type, and the patient's overall health. Recent studies highlight the critical role of lymphatic endothelial cells (LECs) in the tumor microenvironment. Perturbations in LEC function in gastric cancer, marked by aberrant activation or damage, disrupt lymphatic fluid dynamics and impede immune cell infiltration, thereby modulating tumor progression and patient prognosis.
View Article and Find Full Text PDFRen Fail
December 2025
Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
The Grams model, designed to predict adverse event risks in advanced chronic kidney disease (CKD) patients, was evaluated in a Chinese cohort of 1,333 patients with eGFR below 30 mL/min/1.73 m. The model demonstrated moderate to good discrimination across outcomes, performing well in predicting kidney replacement therapy (KRT) but overestimating the risks of cardiovascular disease (CVD) and mortality.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
September 2025
Faculdade de Engenharia da Universidade do Porto, INESC TEC, Porto, Portugal.
Food waste generated throughout the food supply chain raises several environmental, social, and economic issues. Quantitative methods can aid in managing food waste by describing current contexts, predicting future scenarios, and improving related operations. However, a literature review on the use of quantitative methods, specifically the descriptive, predictive, and prescriptive dimensions, to assess and prevent food waste is lacking.
View Article and Find Full Text PDFBr J Cancer
September 2025
Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, E1 1HH, UK.
Background: Multi-cancer detection (MCED) blood tests have the potential to screen for early-stage cancers. Understanding how people experience an MCED cancer signal result is vital prior to any future implementation. We explored experiences in a trial context.
View Article and Find Full Text PDFNat Biomed Eng
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations.
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