The type I interferon (IFN) response is one of the primary defense systems against various pathogens. Although rubella virus (RuV) infection is known to cause dysfunction of various organs and systems, including the central nervous system, little is known about how human neural cells evoke protective immunity against RuV infection, leading to controlling RuV replication. Using cultured human neural cells experimentally infected with RuV RA27/3 strain, we characterized the type I IFN immune response against the virus. RuV infected cultured human neural cell lines and induced IFN-β production, leading to the activation of signal transducer and activator of transcription 1 (STAT1) and the increased expression of IFN-stimulated genes (ISGs). Melanoma-differentiation-associated gene 5 (MDA5), one of the cytoplasmic retinoic acid-inducible gene I (RIG-I)-like receptors, is required for the RuV-triggered IFN-β mRNA induction in U373MG cells. We also showed that upregulation of RuV-triggered ISGs was attenuated by blocking IFN-α/β receptor subunit 2 (IFNAR2) using an IFNAR2-specific neutralizing antibody or by repressing mitochondrial antiviral signaling protein (MAVS) expression using MAVS-targeting short hairpin RNA (shRNA). Furthermore, treating RuV-infected cells with BX-795, a TANK-binding kinase 1 (TBK1)/I kappa B kinase ε (IKKε) inhibitor, robustly reduced STAT1 phosphorylation and expression of ISGs, enhancing viral gene expression and infectious virion production. Overall, our findings suggest that the RuV-triggered type I IFN-mediated antiviral response is essential in controlling RuV gene expression and viral replication in human neural cells.
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http://dx.doi.org/10.3390/ijms23179799 | DOI Listing |
Neural Comput
January 2025
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200437, China
Spiking neural networks (SNNs) have attracted significant interest in the development of brain-inspired computing systems due to their energy efficiency and similarities to biological information processing. In contrast to continuous-valued artificial neural networks, which produce results in a single step, SNNs require multiple steps during inference to achieve a desired accuracy level, resulting in a burden in real-time response and energy efficiency. Inspired by the tradeoff between speed and accuracy in human and animal decision-making processes, which exhibit correlations among reaction times, task complexity, and decision confidence, an inquiry emerges regarding how an SNN model can benefit by implementing these attributes.
View Article and Find Full Text PDFPLoS One
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Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States of America.
Complex systems, such as in brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often difficult. We propose using a Vector Autoregressive model with eXogenous input (VARX) to capture delayed interactions between internal and external variables.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China.
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Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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View Article and Find Full Text PDFJMIR Perioper Med
January 2025
Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States.
Background: Postoperative delirium (POD) is a common complication after major surgery and is associated with poor outcomes in older adults. Early identification of patients at high risk of POD can enable targeted prevention efforts. However, existing POD prediction models require inpatient data collected during the hospital stay, which delays predictions and limits scalability.
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