Non-neutralizing antibodies to SARS-Cov-2-related linear epitopes induce psychotic-like behavior in mice

ObjectiveAn increasing number of studies have reported that numerous patients with coronavirus disease 2019 (COVID-19) and vaccinated individuals have developed central nervous system (CNS) symptoms, and that most of the antibodies in their sera have no virus-neutralizing ability. We tested the hypothesis that non-neutralizing anti-S1-111 IgG induced by the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could negatively affect the CNS.MethodsAfter 14-day acclimation, the grouped ApoE-/- mice were immunized four times (day 0, day 7, day 14, day 28) with different spike-protein-derived peptides (coupled with KLH) or KLH via subcutaneous injection. Antibody level, state of glial cells, gene expression, prepulse inhibition, locomotor activity, and spatial working memory were assessed from day 21.ResultsAn increased level of anti-S1-111 IgG was measured in their sera and brain homogenate after the immunization. Crucially, anti-S1-111 IgG increased the density of microglia, activated microglia, and astrocytes in the hippocampus, and we observed a psychomotor-like behavioral phenotype with defective sensorimotor gating and impaired spontaneity among S1-111-immunized mice. Transcriptome profiling showed that up-regulated genes in S1-111-immunized mice were mainly associated with synaptic plasticity and mental disorders.DiscussionOur results show that the non-neutralizing antibody anti-S1-111 IgG induced by the spike protein caused a series of psychotic-like changes in model mice by activating glial cells and modulating synaptic plasticity. Preventing the production of anti-S1-111 IgG (or other non-neutralizing antibodies) may be a potential strategy to reduce CNS manifestations in COVID-19 patients and vaccinated individuals

Exploring the application and challenges of fNIRS technology in early detection of Parkinson’s disease

BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.ObjectiveThe goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.MethodsUsing an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.ResultsThe SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.ConclusionThe model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region

Data_Sheet_1_Non-neutralizing antibodies to SARS-Cov-2-related linear epitopes induce psychotic-like behavior in mice.docx

ObjectiveAn increasing number of studies have reported that numerous patients with coronavirus disease 2019 (COVID-19) and vaccinated individuals have developed central nervous system (CNS) symptoms, and that most of the antibodies in their sera have no virus-neutralizing ability. We tested the hypothesis that non-neutralizing anti-S1-111 IgG induced by the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could negatively affect the CNS.MethodsAfter 14-day acclimation, the grouped ApoE-/- mice were immunized four times (day 0, day 7, day 14, day 28) with different spike-protein-derived peptides (coupled with KLH) or KLH via subcutaneous injection. Antibody level, state of glial cells, gene expression, prepulse inhibition, locomotor activity, and spatial working memory were assessed from day 21.ResultsAn increased level of anti-S1-111 IgG was measured in their sera and brain homogenate after the immunization. Crucially, anti-S1-111 IgG increased the density of microglia, activated microglia, and astrocytes in the hippocampus, and we observed a psychomotor-like behavioral phenotype with defective sensorimotor gating and impaired spontaneity among S1-111-immunized mice. Transcriptome profiling showed that up-regulated genes in S1-111-immunized mice were mainly associated with synaptic plasticity and mental disorders.DiscussionOur results show that the non-neutralizing antibody anti-S1-111 IgG induced by the spike protein caused a series of psychotic-like changes in model mice by activating glial cells and modulating synaptic plasticity. Preventing the production of anti-S1-111 IgG (or other non-neutralizing antibodies) may be a potential strategy to reduce CNS manifestations in COVID-19 patients and vaccinated individuals.</p