Recent evidence suggests that CC chemokine ligand 20 (CCL20) is upregulated after subarachnoid hemorrhage (SAH). Here, we investigated the functions of CCL20 in SAH injury and its underlying mechanisms of action. We found that CCL20 is upregulated in an SAH mouse model and in cultured primary microglia and neurons. CCL20-neutralizing antibody alleviated SAH-induced neurological deficits, decreased brain water content and neuronal apoptosis, and repressed microglial activation. We observed increased levels of CCL20, CC chemokine receptor 6 (CCR6), interleukin 1 beta (IL-1β), and tumor necrosis factor alpha (TNF-α), as well as of microglial activation in microglia treated with oxyhemoglobin (OxyHb). CCL20 or CCR6 knockdown reversed the effects of OxyHb on microglia. Conditioned medium from OxyHb-treated microglia induced neuronal apoptosis, while the percentage of apoptotic neurons in the conditioned medium from microglia transfected with CCL20 siRNA or CCR6 siRNA was decreased. We observed no decrease in OxyHb-induced apoptosis in CCL20-knockdown neurons. Conditioned medium from OxyHb-treated neurons led to microglial activation and induced CCR6, IL-1β and TNF-α expression, while CCL20 knockdown in neurons or CCR6 knockdown in microglia reversed those effects. Our results thus suggest CCL20 may be targeted to elicit therapeutic benefits after SAH injury.

Glioblastoma multiforme (GBM) is the most malignant primary tumour in the central nervous system, but the molecular mechanisms underlying its pathogenesis remain unclear. In this study, data set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50161 was used to construct a co‐expression network for weighted gene co‐expression network analysis. Two modules (dubbed brown and turquoise) were found to have the strongest correlation with GBM. Functional enrichment analysis indicated that the brown module was involved in the cell cycle, DNA replication, and pyrimidine metabolism. The turquoise module was primarily related to circadian rhythm entrainment, glutamatergic synapses, and axonal guidance. Hub genes were screened by survival analysis using The Cancer Genome Atlas and Human Protein Atlas databases and further tested using the http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4290 and Gene Expression Profiling Interactive Analysis databases. The eight hub genes (NUSAP1, SHCBP1, KNL1, SULT4A1, SLC12A5, NUF2, NAPB, and GARNL3) were verified at both the transcriptional and translational levels, and these gene expression levels were significant based on the World Health Organization classification system. These hub genes may be potential biomarkers and therapeutic targets for the accurate diagnosis and management of GBM.

To evaluate the edema area around basal ganglia hemorrhage by the application of computerized tomography (CT)-based radiomics as a prognostic factor and improve the diagnosis efficacy, a total of 120 patients with basal ganglia hemorrhage were analyzed retrospectively. The texture analysis software Mazda 3.3 was used to preprocess the CT images and manually sketch the region of interest to extract the texture features. The extracted texture features were selected by Fisher coefficient, POE+ACC and mutual information. The texture discriminant analysis uses the B11 module in the Mazda 3.3 software. The data were randomly divided into a training dataset (67%) and test dataset (33%). To further study the texture features, the training dataset can be divided into groups according to the median of GCS score, NIHSS score, and maximum diameter of hematoma. Random forest model, support vector machine model, and neural network model were built. AUC of the receiver operating characteristics curve was used to assess the performance of models with test dataset. Among all texture post-processing methods, the lowest error rate was 2.22% for the POE+ACC/nonlinear discriminant. For the maximum diameter of hematoma, GCS score, and NIHSS score group, the lowest error rate were 26.66%, 23.33%, and 30.00%, respectively. The values of AUCs were 0.87, 0.81, and 0.76, for random forest model, support vector machine model, and neural network model in the test dataset, respectively. Radiomic method with proper model may have a potential role in predicting the edema area around basal ganglia hemorrhage. It can be used as a secondary group in the diagnosis of edema area around basal ganglia hemorrhage.

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