Search alternatives:
marked decrease » marked increase (Expand Search)
large decrease » larger decrease (Expand Search), large increases (Expand Search), large degree (Expand Search)
incl decrease » nn decrease (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
marked decrease » marked increase (Expand Search)
large decrease » larger decrease (Expand Search), large increases (Expand Search), large degree (Expand Search)
incl decrease » nn decrease (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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<b>Effect of Marked Weight Loss on Adipose Tissue Biology in People with Obesity and Type 2 Diabetes</b>
Published 2025“…</p><p dir="ltr"><b>Results: </b>Weight loss: <a href="" target="_blank">i) </a><a href="" target="_blank">decreased adipose tissue </a>expression of genes related to extracellular matrix remodeling; ii) decreased adipose tissue expression of SERPINE 1 which encodes plasminogen activator inhibitor-1 (PAI-1); iii) did not decrease adipose tissue immune cell content or expression of genes involved in inflammation; iv) decreased adipose tissue ceramide content; v) decreased plasma <a href="" target="_blank">PAI-1 </a>and leptin concentrations and increased plasma high-molecular weight (HMW) adiponectin; and vi) decreased plasma small extracellular vesicle (sEV) concentration and the sEV content of microRNAs proposed to inhibit insulin action, and completely reversed the inhibitory effect of plasma sEVs on insulin signaling in myotubes.…”
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ROC analysis to mark selectivity results in mostly mixed-selective units.
Published 2025“…<b>b</b> There do not seem to be any significant differences between fast and slow groups using the ROC definition, likely because almost all neurons are marked to be mixed selective as compared to our rate-based classification approach. …”
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Data Sheet 1_Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.pdf
Published 2025“…Stress monitoring may be supported by valid and reliable machine-learning algorithms. However, investigation of algorithms detecting stress severity on a continuous scale is missing due to high demands on data quality for such analyses. …”