Increased expression of the 3.1 isoform of the KCNH2 potassium channel has been associated with cognitive dysfunction and with schizophrenia, yet little is known about the underlying pathophysiological mechanisms. Here, by using in vivo wireless local field potential recordings during working memory processing, in vitro brain slice whole-cell patching recordings and in vivo stereotaxic hippocampal injection of AAV-encoded expression, we identified specific and delayed disruption of hippocampal-mPFC synaptic transmission and functional connectivity associated with reductions of SERPING1, CFH, and CD74 in the KCNH2-3.1 overexpression transgenic mice. The differentially expressed genes in mice are enriched in neurons and microglia, and reduced expression of these genes dysregulates the complement cascade, which has been previously linked to synaptic plasticity. We find that knockdown of these genes in primary neuronal-microglial cocultures from KCNH2-3.1 mice impairs synapse formation, and replenishing reduced CFH gene expression rescues KCNH2-3.1-induced impaired synaptogenesis. Translating to humans, we find analogous dysfunctional interactions between hippocampus and prefrontal cortex in coupling of the fMRI blood oxygen level-dependent (BOLD) signal during working memory in healthy subjects carrying alleles associated with increased KCNH2-3.1 expression in brain. Our data uncover a previously unrecognized role of the truncated KCNH2-3.1 potassium channel in mediating complement activation, which may explain its association with altered hippocampal-prefrontal connectivity and synaptic function. These results provide a potential molecular link between increased KCNH2-3.1 expression, synapse alterations, and hippocampal-prefrontal circuit abnormalities implicated in schizophrenia.
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http://dx.doi.org/10.1038/s41380-019-0530-1 | DOI Listing |
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFSeizure
December 2024
University College Hospital, London, UK; UCL Queen Square Institute of Neurology: Department of Clinical and Experimental Epilepsy, London WC1N 3BG, UK. Electronic address:
Objective: Professional bodies recommend the use of performance validity tests (PVTs) to aid the interpretation of scores obtained in neuropsychological assessments, but base rates of failure differ according to neurological diagnosis and the associated impairments. This review summarises the PVT literature in people with epilepsy with the aim of establishing base rates of PVT failure and the factors associated with PVT performance in this population.
Methods: Ovid and PubMed databases were searched for studies reporting PVT test performance in people with epilepsy.
Comput Biol Med
January 2025
LMA Laboratory, University of Bejaia, Bejaia 06000, Algeria. Electronic address:
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.
View Article and Find Full Text PDFInt J Eat Disord
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Objective: Difficulty updating information in working memory has been proposed to underlie ruminative thinking in individuals with anorexia nervosa (AN). However, evidence regarding updating difficulties in AN remains inconclusive, particularly among adolescents. It has been proposed that exposure to negative emotion and disorder-salient stimuli may uniquely influence updating in AN.
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