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Cross-sectional data from a sample of older adults with obesity was used to determine how peripheral insulin resistance (PIR) and neuronal insulin signaling abnormalities (NISAs) relate to executive function and functional brain network topology. Older adults (n = 71) with obesity but without type 2 diabetes were included. PIR was quantified by HOMA2-IR. NISAs were quantified according to an established neuron-derived small-extracellular-vesicle-based metric, R. An executive function composite score, summed scores to the Auditory Verbal Learning Test (AVLT) trials 1-5, and functional brain networks generated from resting-state functional magnetic resonance imaging data were outcomes in analyses. We used general linear models and a novel regression framework for brain network analysis to identify relationships between insulin-related biomarkers and brain-related outcomes. HOMA2-IR, but not R, was negatively associated with executive function. Neither measure was associated with AVLT score. HOMA2-IR was also related to hippocampal network topology in participants who had undergone functional neuroimaging. Neither HOMA2-IR nor R were significantly related to network topology of the central executive network. This study provides further evidence that PIR is associated with aging brain function. NISAs were not found to be related to PIR, cognition, or functional brain network topology.
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http://dx.doi.org/10.1038/s41598-025-06038-1 | DOI Listing |
iScience
September 2025
Max Planck Institute of Psychiatry, 80804 Munich, Germany.
Isoform-specific expression patterns have been linked to stress-related psychiatric disorders such as major depressive disorder (MDD). To further explore their involvement, we constructed co-expression networks using total gene expression (TE) and isoform ratio (IR) data from affected ( = 210, 81% with depressive symptoms) and unaffected ( = 95) individuals. Networks were validated using advanced graph generation methods.
View Article and Find Full Text PDFAsian J Psychiatr
September 2025
National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Traditional Chinese Medicine Re
Background: Amnestic mild cognitive impairment (aMCI) is characterized by marked episodic memory decline. The hippocampus is essential for episodic memory, and integration of information within its subregions is central to this process. This study examined how alterations in hippocampal subregional network relate to episodic memory impairment in aMCI.
View Article and Find Full Text PDFJ Affect Disord
September 2025
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China. Electronic address:
Major Depressive Disorder (MDD) poses a significant global health threat, impairing individual functioning and increasing socioeconomic burden. Accurate diagnosis is crucial for improving treatment outcomes. This study proposes Time-Frequency Text-Attributed DeepWalk (TF-TADW), a framework for MDD classification using resting-state functional MRI data.
View Article and Find Full Text PDFChemSusChem
September 2025
Institute of Organic Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081, Ulm, Germany.
Organic battery electrode materials represent a sustainable alternative compared to most inorganic electrodes, yet challenges persist regarding their energy density and cycling stability. In this work, a new organic electrode material is described, which is obtained via ionothermal polymerization of low-cost starting materials, melem (2,5,8-triamino-tri-s-triazine) and perylenetetracarboxylic dianhydride (PTCDA). The resulting networked polymer Melem-PDI exhibits favorable thermal and electrochemical properties, prompting investigation into its performance as a positive electrode material in rechargeable lithium and magnesium batteries.
View Article and Find Full Text PDFACS Omega
September 2025
Shandong Provincial Key Laboratory of Oil, Gas and New Energy Storage and Transportation Safety, China University of Petroleum, Qingdao, Shandong 266580, People's Republic of China.
The natural gas pipeline network has a complex topology with variable flow directions, and the supply demand relationships between nodes exhibit cyclical, fluctuating, and time-varying trends. Developing efficient, accurate, and fast intelligent control algorithms is crucial for optimizing the distribution of natural gas networks. Analyzing the operational data from a provincial network over three years revealed that abnormal flow data, such as supply interruptions due to incidents, early fulfillment of supply, and insufficient flow distribution, can cause deviations between the actual transmission volume and the planned transmission volume predicted by the uneven coefficient method.
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