Learning and memory deficits, including spatial navigation difficulties, are common in autism spectrum disorder (ASD). Several ASD mouse models (, , ) exhibit impaired spatial learning, with these deficits often attributed to hippocampal dysfunction. However, we identify the perirhinal cortex (PRC) as a critical driver of these deficits. Cortical-wide reduction in excitatory neurons replicated the spatial learning and long-term potentiation (LTP) impairments-a cellular correlate of learning-seen in mice, while hippocampal-wide reduction did not. PRC-specific viral-mediated reduction in excitatory neurons decreased release probability, which consequently disrupted synaptic transmission and LTP in the hippocampus, as well as spatial learning. As PRC activity was reduced, chemogenetic activation of the PRC reversed these deficits in mice and rescued spatial learning and LTP impairments in and knockout mice. Thus, in several genetic models of ASD, PRC abnormalities may disrupt hippocampal function to impair learning and memory.
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http://dx.doi.org/10.1126/sciadv.adt0780 | DOI Listing |
Ecotoxicol Environ Saf
March 2025
Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China. Electronic address:
Skeletal fluorosis caused by coal-burning type endemic fluorosis greatly affects the health of the population in the affected areas, but large-scale diagnostic work is limited by human and material resources, making it difficult to implement comprehensively. In this study, we investigate adults in coal-burning type endemic skeletal fluorosis areas in Guizhou. The study areas are selected by a comprehensive analysis of the detection rate of dental fluorosis in children aged 8-12 years in Guizhou for the year 2023.
View Article and Find Full Text PDFArtif Intell Med
March 2025
Department of Advanced Computing Sciences, Maastricht University, The Netherlands. Electronic address:
Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting from body surface potentials. This is classically based on geometric information on the torso and the heart from imaging, which complicates clinical application. In this study, we aim to develop a deep learning framework to estimate heart surface potentials solely from body surface potentials, enabling wider clinical use.
View Article and Find Full Text PDFComput Biol Chem
March 2025
Scientific Research Management Department, Shanghai University, Shanghai, 200444, China. Electronic address:
Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function".
View Article and Find Full Text PDFMed Image Anal
March 2025
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China. Electronic address:
Text-guided visual understanding is a potential solution for downstream task learning in echocardiography. It can reduce reliance on labeled large datasets and facilitate learning clinical tasks. This is because the text can embed highly condensed clinical information into predictions for visual tasks.
View Article and Find Full Text PDFComput Methods Programs Biomed
March 2025
School of Information Engineering, Shenyang University, Shenyang 110044, China.
Background And Objective: Virtual reality motion sickness is a significant barrier to the widespread adoption of virtual reality technology. Current virtual reality motion sickness detection methods using EEG signals often fail to identify comprehensive neuro-markers and lack generalizability across multiple subjects.
Methods: To address this issue, we analyzed the pre- and post-induction phases of virtual reality motion sickness, as well as the induction process, from multiple domain features.
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