Stroke is a main cause of disability and death among adults in China, and acute ischemic stroke accounts for 80% of cases. The key to ischemic stroke treatment is to recanalize the blocked blood vessels. However, more than 90% of patients cannot receive effective treatment within an appropriate time, and delayed recanalization of blood vessels causes reperfusion injury. Recent research has revealed that ischemic postconditioning has a neuroprotective effect on the brain, but the mechanism has not been fully clarified. Long non-coding RNAs (lncRNAs) have previously been associated with ischemic reperfusion injury in ischemic stroke. LncRNAs regulate important cellular and molecular events through a variety of mechanisms, but a comprehensive analysis of potential lncRNAs involved in the brain protection produced by ischemic postconditioning has not been conducted. In this review, we summarize the common mechanisms of cerebral injury in ischemic stroke and the effect of ischemic postconditioning, and we describe the potential mechanisms of some lncRNAs associated with ischemic stroke.

Background: Several complex cellular and gene regulatory processes are involved in peripheral nerve repair. This study uses bioinformatics to analyze the differentially expressed genes (DEGs) in the satellite glial cells of mice following sciatic nerve injury. Methods: R software screens differentially expressed genes, and the WebGestalt functional enrichment analysis tool conducts Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway analysis. The Search Tool for the Retrieval of Interacting Genes/Proteins constructs protein interaction networks, and the cytoHubba plug-in in the Cytoscape software predicts core genes. Subsequently, the sciatic nerve injury model of mice was established and the dorsal root ganglion satellite glial cells were isolated and cultured. Satellite glial cells-related markers were verified by immunofluorescence staining. Real-time polymerase chain reaction assay and Western blotting assay were used to detect the mRNA and protein expression of Sox9 in satellite glial cells. Results: A total of 991 DEGs were screened, of which 383 were upregulated, and 508 were downregulated. The GO analysis revealed the processes of biosynthesis, negative regulation of cell development, PDZ domain binding, and other biological processes were enriched in DEGs. According to the KEGG pathway analysis, DEGs are primarily involved in steroid biosynthesis, hedgehog signaling pathway, terpenoid backbone biosynthesis, American lateral skeleton, and melanoma pathways. According to various cytoHubba algorithms, the common core genes in the protein–protein interaction network are Atf3, Mmp2, and Sox9. Among these, Sox9 was reported to be involved in the central nervous system and the generation and development of astrocytes and could mediate the transformation between neurogenic and glial cells. The experimental results showed that satellite glial cell marker GS were co-labeled with Sox9; stem cell characteristic markers Nestin and p75NTR were labeled satellite glial cells. The mRNA and protein expression of Sox9 in satellite glial cells were increased after sciatic nerve injury. Conclusions: In this study, bioinformatics was used to analyze the DEGs of satellite glial cells after sciatic nerve injury, and transcription factors related to satellite glial cells were screened, among which Sox9 may be associated with the fate of satellite glial cells.

Embolic stroke (ES) is characterized by high morbidity and mortality. Its mortality predictors remain unclear. This study aimed to use machine learning (ML) to identify the key predictors of mortality for ES patients in the intensive care unit (ICU). Data were extracted from two large ICU databases: MIMIC-IV for training and internal validation, and eICU-CRD for external validation. We developed predictive models of ES mortality based on 15 ML algorithms. We relied on the synthetic minority oversampling technique (SMOTE) to address class imbalance. Our main performance metric was area under the ROC (AUROC). We adopted recursive feature elimination (RFE) for feature selection. We assessed model performance using three disease-severity scoring systems as benchmarks. Of the 1,566 and 207 ES patients enrolled in the two databases, there were 173 (15.70%), 73 (15.57%), and 36 (17.39%) hospital mortality in the training, internal validation, and external validation cohort, respectively. The random forest (RF) model had the largest AUROC (0.806) in the internal validation phase and was chosen as the best model. The AUROC of the RF compact (RF-COM) model containing the top six features identified by RFE was 0.795. In the external validation phase, the AUROC of the RF model was 0.838, and the RF-COM model was 0.830, outperforming other models. Our findings suggest that the RF model was the best model and the top six predictors of ES hospital mortality were Glasgow Coma Scale, white blood cell, blood urea nitrogen, bicarbonate, age, and mechanical ventilation.

To explore the difference of gene expression between undifferentiated hESCs and DRG induced by hESCs differentiation for 1 day by bioinformatics technology, and find out the key differential genes and their potential molecular regulation mechanism; The functions involved are analyzed and predicted. Download the gene chip data series GSE75582 related to stem cells and dorsal root ganglion from the geo expression database, import the gene chip online analysis tool GEO2R, screen out the differential genes, and construct the volcano map. Webgestalt database was used for GO function analysis and KEGG pathway enrichment analysis, and string analysis and Cytoscape software were used to construct PPI network. Results 4102 differentially expressed genes were screened, of which 2674 were up-regulated and 1428 were down regulated. Bioinformatics analysis of differentially expressed genes showed that the genes were mainly enriched in axon guidance, signaling pathways regulating pluripotency of stem cells, pathways in cancer, ECM receiver interaction, focal adhesion, cell adhesion molecules (CAMs), PI3K Akt signaling pathway, MAPK signaling pathway, TGF beta signaling pathway Ras signaling pathway, which extracts differential genes in stem cell related signaling pathways, including 34 up-regulated genes and 13 down regulated genes. Conclusion BMP4, Sox2, FGF2, Nanog, Smad3, KLF4, gsk3b and FGFR2 in undifferentiated hESCs are closely related to stem cell self-renewal; BMP4, Sox2, FGF2, Smad3, gsk3b and FGFR2 were mostly related to neuropathic pain and neurite growth. However, the functions of these key genes need to be further confirmed by future experimental studies.