In this study, a model of migraine was established by electrical stimulation of the superior sagittal sinus in rats. These rats were then treated orally with paroxetine at doses of 2.5, 5, or 10 mg/kg per day for 14 d...In this study, a model of migraine was established by electrical stimulation of the superior sagittal sinus in rats. These rats were then treated orally with paroxetine at doses of 2.5, 5, or 10 mg/kg per day for 14 days. Following treatment, mechanical withdrawal thresholds were significantly higher, extracellular concentrations of 5-hydroxytryptamine in the periaqueductal grey matter and nucleus reticularis gigantocellularis were higher, and the expression of phosphorylated p38 in the trigeminal nucleus caudalis was lower. Our experimental findings suggest that paroxetine has analgesic effects in a rat migraine model, which are mediated by inhibition of p38 phosphorylation.展开更多
Each cell possesses a unique gene regulatory network.However,limited methods exist for inferring cell-specific regulatory networks,particularly through the integration of single-cell RNA sequencing(scRNA-seq)and singl...Each cell possesses a unique gene regulatory network.However,limited methods exist for inferring cell-specific regulatory networks,particularly through the integration of single-cell RNA sequencing(scRNA-seq)and single-cell assay for transposase-accessible chromatin using sequencing(scATAC-seq)data.Herein,we develop a novel algorithm,named single-cell regulatory network inference(ScReNI),for inferring gene regulatory networks at the single-cell level.In ScReNI,the nearest neighbors algorithm is utilized to establish the neighboring cells for each cell,where nonlinear regulatory relationships between gene expression and chromatin accessibility are inferred through a modified random forest.ScReNI is designed to analyze both paired and unpaired datasets for scRNA-seq and scATAC-seq.ScReNI demonstrates more accurate regulatory relationships and outperforms existing cell-specific network inference methods in network-based cell clustering.ScReNI also shows superior performance in inferring cell type-specific regulatory networks through integrating gene expression and chromatin accessibility.Importantly,ScReNI offers the unique function of identifying cell-enriched regulators based on each cell-specific network.Overall,ScReNI facilitates the inference of cell-specific regulatory networks and cell-enriched regulators,providing insights into single-cell regulatory mechanisms of diverse biological processes.ScReNI is available at https://github.com/Xuxl2020/ScReNI.展开更多
文摘In this study, a model of migraine was established by electrical stimulation of the superior sagittal sinus in rats. These rats were then treated orally with paroxetine at doses of 2.5, 5, or 10 mg/kg per day for 14 days. Following treatment, mechanical withdrawal thresholds were significantly higher, extracellular concentrations of 5-hydroxytryptamine in the periaqueductal grey matter and nucleus reticularis gigantocellularis were higher, and the expression of phosphorylated p38 in the trigeminal nucleus caudalis was lower. Our experimental findings suggest that paroxetine has analgesic effects in a rat migraine model, which are mediated by inhibition of p38 phosphorylation.
基金supported by the National Natural Science Foundation of China(Grant Nos.T2222003 and 32170849)the National Key Research and Development Program of China(Grant No.2022YFA1105400)the Science and Technology Planning Project of Guangdong Province(Grant Nos.2023B1212060050 and 2020B1212060052).
文摘Each cell possesses a unique gene regulatory network.However,limited methods exist for inferring cell-specific regulatory networks,particularly through the integration of single-cell RNA sequencing(scRNA-seq)and single-cell assay for transposase-accessible chromatin using sequencing(scATAC-seq)data.Herein,we develop a novel algorithm,named single-cell regulatory network inference(ScReNI),for inferring gene regulatory networks at the single-cell level.In ScReNI,the nearest neighbors algorithm is utilized to establish the neighboring cells for each cell,where nonlinear regulatory relationships between gene expression and chromatin accessibility are inferred through a modified random forest.ScReNI is designed to analyze both paired and unpaired datasets for scRNA-seq and scATAC-seq.ScReNI demonstrates more accurate regulatory relationships and outperforms existing cell-specific network inference methods in network-based cell clustering.ScReNI also shows superior performance in inferring cell type-specific regulatory networks through integrating gene expression and chromatin accessibility.Importantly,ScReNI offers the unique function of identifying cell-enriched regulators based on each cell-specific network.Overall,ScReNI facilitates the inference of cell-specific regulatory networks and cell-enriched regulators,providing insights into single-cell regulatory mechanisms of diverse biological processes.ScReNI is available at https://github.com/Xuxl2020/ScReNI.
