Introduction: Intracerebral hemorrhage (ICH) is the most fatal type of stroke worldwide. Herein, we aim to develop a predictive model based on computed tomography (CT) markers in an ICH cohort and validate it in another cohort. Methods: This retrospective observational cohort study was conducted in 3 medical centers in China. The values of CT markers, including hypodensities, hematoma density, blend sign, black hole sign, island sign, midline shift, baseline hematoma volume, and satellite sign, in predicting poor outcome were analyzed by logistic regression analysis. A nomogram was developed based on the results of multivariate logistic regression analysis in development cohort. Area under curve (AUC) and calibration plot were used to assess the accuracy of nomogram in this development cohort and validate in another cohort. Results: A total of 1,498 patients were included in this study. Multivariate logistic regression analysis indicated that hypodensities, black hole sign, island sign, midline shift, and baseline hematoma volume were independently associated with poor outcome in development cohort. The AUC was 0.75 (95% confidence interval [CI]: 0.73–0.76) in the internal validation with development cohort and 0.74 (95% CI: 0.72–0.75) in the external validation with validation cohort. The calibration plot in development and validation cohort indicated that the nomogram was well calibrated. Conclusions: CT markers of hypodensities, black hole sign, and island sign might predict poor outcome of ICH patients within 90 days.

PurposeGlioblastoma multiforme (GBM) is a common and aggressive form of brain tumor. The N6-methyladenosine (m6A) mRNA modification plays multiple roles in many biological processes and disease states. However, the relationship between m6A modifications and the tumor microenvironment in GBM remains unclear, especially at the single-cell level.Experimental DesignSingle-cell and bulk RNA-sequencing data were acquired from the GEO and TCGA databases, respectively. We used bioinformatics and statistical tools to analyze associations between m6A regulators and multiple factors.ResultsHNRNPA2B1 and HNRNPC were extensively expressed in the GBM microenvironment. m6A regulators promoted the stemness state in GBM cancer cells. Immune-related BP terms were enriched in modules of m6A-related genes. Cell communication analysis identified genes in the GALECTIN signaling network in GBM samples, and expression of these genes (LGALS9, CD44, CD45, and HAVCR2) correlated with that of m6A regulators. Validation experiments revealed that MDK in MK signaling network promoted migration and immunosuppressive polarization of macrophage. Expression of m6A regulators correlated with ICPs in GBM cancer cells, M2 macrophages and T/NK cells. Bulk RNA-seq analysis identified two expression patterns (low m6A/high ICP and high m6A/low ICP) with different predicted immune infiltration and responses to ICP inhibitors. A predictive nomogram model to distinguish these 2 clusters was constructed and validated with excellent performance.ConclusionAt the single-cell level, m6A modification facilitates the stemness state in GBM cancer cells and promotes an immunosuppressive microenvironment through ICPs and the GALECTIN signaling pathway network. And we also identified two m6A-ICP expression patterns. These findings could lead to novel treatment strategies for GBM patients.

Objective: To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracerebral hemorrhage(ICH). Methods: The clinical and CT image data of the patients with ICH in Qinghai Provincial People's Hospital and Xuzhou Central Hospital were collected. The risk factors related to the poor outcome of the patients were determined by univariate and multivariate logistic regression analysis. To determine the effect of factors related to poor outcome, the nomogram model was made by software of R 3.5.2 and the support vector machine operation was completed by software of SPSS Modelor. Results: A total of 8265 patients were collected and 1186 patients met the criteria of the study. Age, hospitalization days, blend sign, intraventricular extension, subarachnoid hemorrhage, midline shift, diabetes and baseline hematoma volume were independent predictors of poor outcome. Among these factors, baseline hematoma volume20ml (odds ratio:13.706, 95% confidence interval:9.070-20.709, p < 0.001) was the most significant factor for poor outcome, followed by the volume among 10ml-20ml (odds ratio:11.834, 95% confidence interval:7.909-17.707, p < 0.001). It was concluded that the highest percentage of weight in outcome was baseline hematoma volume (25.0%), followed by intraventricular hemorrhage (23.0%). Conclusion: This predictive model might accurately predict the outcome of patients with ICH. It might have a wide range of application prospects in clinical.

To conduct a systematic review and meta-analysis and evaluate the effect of tranexamic acid in patients with traumatic brain injury. PubMed, EMBASE, and CENTRAL (Cochrane Central Register of Controlled Trials) were searched to identify randomized controlled trials and evaluate the effect of tranexamic acid in traumatic brain injury patients. The primary outcome was mortality. Two reviewers extracted the data independently. The random effect meta-analysis was used to estimate the aggregate effect size of 95% confidence intervals. Six randomized controlled trials investigating tranexamic acid versus placebo and 30073 patients were included. Compared with placebo, tranexamic acid decreased the mortality (RR = 0.92; 95% CI, 0.87-0.96; p < 0.001) and growth of hemorrhagic mass (RR = 0.78; 95% CI, 0.61-0.99; p = 0.04). However, tranexamic acid could not decrease disability or independent, neurosurgery, vascular embolism, and stroke. Current evidence suggested that compared with placebo, tranexamic acid could reduce mortality and growth of hemorrhagic mass. This finding indicated that patients with traumatic brain injury should be treated with tranexamic acid.

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Injuries to the central nervous system (CNS) often lead to severe neurological dysfunction and even death. However, there are still no effective measures to improve functional recovery following CNS injuries. Optogenetics, an ideal method to modulate neural activity, has shown various advantages in controlling neural circuits, promoting neural remapping, and improving cell survival. In particular, the emerging technique of optogenetics has exhibited promising therapeutic methods for CNS injuries. In this review, we introduce the light-sensitive proteins and light stimulation system that are important components of optogenetic technology in detail and summarize the development trends. In addition, we construct a comprehensive picture of the current application of optogenetics in CNS injuries and highlight recent advances for the treatment and functional recovery of neurological deficits. Finally, we discuss the therapeutic challenges and prospective uses of optogenetics therapy by photostimulation/photoinhibition modalities that would be suitable for clinical applications.

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Aim: Lower grade gliomas [LGGs; World Health Organization (WHO) grades 2 and 3], owing to the heterogeneity of their clinical behavior, present a therapeutic challenge to neurosurgeons. The aim of this study was to explore the N6-methyladenosine (m6A) modification landscape in the LGGs and to develop an m6A-related microRNA (miRNA) risk model to provide new perspectives for the treatment and prognostic assessment of LGGs. Methods: Messenger RNA (mRNA) and miRNA expression data of LGGs were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. An m6A-related miRNA risk model was constructed via least absolute shrinkage and selection operator (LASSO), univariate, and multivariate Cox regression analysis. Next, Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment analysis, immune infiltrate analysis, dynamic nomogram, and drug sensitivity prediction were used to evaluate this risk model. Results: Firstly, six m6A-related miRNAs with independent prognostic value were selected based on clinical information and used to construct a risk model. Subsequently, compared with low-risk group, LGGs in the high-risk group had a higher m6A writer and reader scores, but a lower eraser score. Moreover, LGGs in the high-risk group had a significantly worse clinical prognosis than those in the low-risk group. Simultaneously, this risk model outperformed other clinicopathological variables in the prognosis prediction of LGGs. Immune infiltrate analysis revealed that the proportion of M2 macrophages, regulatory T (Treg) cells, and the expression levels of exhausted immune response markers were significantly higher in the high-risk group than in the low-risk group. Finally, this study constructed an easy-to-use and free dynamic nomogram to help clinicians use this risk model to aid in diagnosis and prognosis assessment. Conclusions: This study developed a m6A-related risk model and uncovered two different m6A modification landscapes in LGGs. Moreover, this risk model may provide guidance and help in clinical prognosis assessment and immunotherapy response prediction for LGGs.