Mitochondrial dysfunction contributes to brain injury following global cerebral ischemia after cardiac arrest. Carbon monoxide treatment has shown potent cytoprotective effects in ischemia/reperfusion injury. This study aimed to investigate the effects of carbon monoxide-releasing molecules on brain mitochondrial dysfunction and brain injury following resuscitation after cardiac arrest in rats. A rat model of cardiac arrest was established by asphyxia. The animals were randomly divided into the following 3 groups: cardiac arrest and resuscitation group, cardiac arrest and resuscitation plus carbon monoxide intervention group, and sham control group (no cardiac arrest). After the return of spontaneous circulation, neurologic deficit scores (NDS) and S-100B levels were significantly decreased at 24, 48, and 72 h, but carbon monoxide treatment improved the NDS and S-100B levels at 24 h and the 3-day survival rates of the rats. This treatment also decreased the number of damaged neurons in the hippocampus CA1 area and increased the brain mitochondrial activity. In addition, it increased mitochondrial biogenesis by increasing the expression of biogenesis factors including peroxisome proliferator-activated receptor-γ coactivator-1α, nuclear respiratory factor-1, nuclear respiratory factor-2 and mitochondrial transcription factor A. Thus, this study showed that carbon monoxide treatment alleviated brain injury after cardiac arrest in rats by increased brain mitochondrial biogenesis.

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.

Background In December 2019, coronavirus disease 2019 (COVID-19) caused by a novel coronavirus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2; previously known as 2019-nCoV) emerged in Wuhan, China, and caused many infections and deaths. At present, there are no specific drugs for the etiology and treatment of COVID-19. A combination of traditional Chinese and western medicine is proposed to treat COVID-19, in which Huang Lian Jie Du decoction (HLJDD) is recommended for the treatment of COVID-19 in many provinces in China and has been widely used in the clinic. This study explored the potential targets of HLJDD in the treatment of COVID-19 based on network pharmacology. Methods First, the chemical composition and targets of HLJDD and COVID-19-related targets were obtained through the TCMSP, UniProt, GeneCards and OMIM databases. Second, HLJDD target and HLJDD-COVID-19 target networks were constructed via the STRING database and Cytoscape software. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the HLJDD-COVID-19 targets was applied via the DAVID database. Results Our study identified a total of 67 active ingredients of HLJDD and 204 targets of HLJDD. A total of 502 COVID-19-related targets were obtained, of which 47 were intersecting targets of HLJDD and COVID-19. A total of 179 GO terms and 77 KEGG terms, including the TNF signaling pathway, NF-κB signaling pathway and HIF-1 signaling pathway, were identified. Conclusion The present study explored the potential targets and signaling pathways of HLJDD during the treatment of COVID-19, which may provide a basis for the research and development of drugs for the treatment of COVID-19.

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