Reperfusion therapy contributes to better clinical outcomes in patients with acute ischemic stroke but carries a more significant risk of hemorrhagic transformation (HT) compared to supportive care. Once HT occurs, the outcome is usually poor and this causes a dilemma in the treatment of ischemic stroke. Consequently, early prediction of HT would be extremely helpful for guiding precise treatment of ischemic stroke. In this review, we focus on summarizing biomarkers of HT and elucidating possible mechanisms so as to identify potential biomarkers for predicting HT.

Background: Glioblastoma (GBM) is considered the most malignant and devastating intracranial tumor without effective treatment. Autophagy, apoptosis, and necrosis, three classically known cell death pathways, can provide novel clinical and immunological insights, which may assist in designing personalized therapeutics. In this study, we developed and validated an effective signature based on autophagy-, apoptosis- and necrosis-related genes for prognostic implications in GBM patients. Methods: Variations in the expression of genes involved in autophagy, apoptosis and necrosis were explored in 518 GBM patients from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were performed to construct a combined prognostic signature. Kaplan–Meier survival, receiver-operating characteristic (ROC) curves and Cox regression analyses based on overall survival (OS) and progression-free survival (PFS) were conducted to estimate the independent prognostic performance of the gene signature. The Chinese Glioma Genome Atlas (CGGA) dataset was used for external validation. Finally, we investigated the differences in the immune microenvironment between different prognostic groups and predicted potential compounds targeting each group. Results: A 16-gene cell death index (CDI) was established. Patients were clustered into either the high risk or the low risk groups according to the CDI score, and those in the low risk group presented significantly longer OS and PFS than the high CDI group. ROC curves demonstrated outstanding performance of the gene signature in both the training and validation groups. Furthermore, immune cell analysis identified higher infiltration of neutrophils, macrophages, Treg, T helper cells, and aDCs, and lower infiltration of B cells in the high CDI group. Interestingly, this group also showed lower expression levels of immune checkpoint molecules PDCD1 and CD200, and higher expression levels of PDCD1LG2, CD86, CD48 and IDO1. Conclusion: Our study proposes that the CDI signature can be utilized as a prognostic predictor and may guide patients’ selection for preferential use of immunotherapy in GBM.

Inappropriate activation or overactivation of cyclic GMP-AMP synthase (cGAS) by double-stranded deoxyribonucleic acid (dsDNA) initiates a regulatory signaling cascade triggering a variety of inflammatory responses, which are a great threat to human health. This study focused on identifying the role of cGAS in atherosclerosis and its potential mechanisms. The relationship between cGAS and atherosclerosis was identified in an ApoE -/- mouse model. Meanwhile, RNA sequencing (RNA-seq) analysis of the underlying mechanisms of atherosclerosis in RAW264.7 macrophages treated with cGAS inhibition was conducted. Results showed that cGAS was positively correlated with atherosclerotic plaque area, and was mainly distributed in macrophages. RNA-seq analysis revealed that inflammatory response, immune response and cytokine-cytokine receptor interaction may play important roles in the development of atherosclerosis. Real-time quantitative polymerase chain reaction (RT-qPCR) results showed that the expression of the pro-inflammatory factors, signal transducer and activator of transcription (Stat), interferon regulatory factor (Irf), toll-like receptors (Tlrs), and type I interferons (Ifns) were synergistically reduced when cGAS was inhibited. Furthermore, cGAS inhibition significantly inhibited RAW264.7 macrophage M1 polarization. These results demonstrate that cGAS may contribute to the development of atherosclerosis through synergistic inflammatory signaling of TLRs, STAT/IRF as well as IFNs, leading to macrophage M1 polarization.

Objective: To develop a risk prediction tool for acute ischemic stroke (AIS) for patients presenting to the emergency department (ED) with acute dizziness/vertigo or imbalance.Method: A prospective, multicenter cohort study was designed, and adult patients presenting with dizziness/vertigo or imbalance within 14 days were consecutively enrolled from the EDs of 4 tertiary hospitals between August 10, 2020, and June 10, 2021. Stroke was diagnosed by CT or MRI performed within 14 days of symptom onset. Participants were followed-up for 30 days. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis was conducted to extract predictive factors that best identified patients at high risk of stroke to establish a prediction model. Model discrimination and calibration were assessed and its prediction performance was compared with the age, blood pressure, clinical features, duration, and diabetes (ABCD2) score, nystagmus scheme, and finger to nose test.Results: In this study, 790 out of 2,360 patients were enrolled {median age, 60.0 years [interquartile range (IQR), 51–68 years]; 354 (44.8%) men}, with complete follow-up data available. AIS was identified in 80 patients. An online web service tool (https://neuroby.shinyapps.io/dynnomapp/) was developed for stroke risk prediction, including the variables of sex, trigger, isolated symptom, nausea, history of brief dizziness, high blood pressure, finger to nose test, and tandem gait test. The model exhibited excellent discrimination with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 (95% CI: 0.855–0.923), compared with the ABCD2 score, nystagmus scheme, and finger to nose test [0.712 (95% CI, 0.652–0.771), 0.602 (95% CI, 0.556–0.648), and 61.7 (95% CI, 0.568–0.666) respectively].Conclusion: Our new prediction model exhibited good performance and could be useful for stroke identification in patients presenting with dizziness, vertigo, or imbalance. Further externally validation study is needed to increase the strength of our findings.

Background: Physicians' knowledge and practice of atrial fibrillation (AF) are determinants of the efficacy of thromboprophylaxis.Hypothesis: This study aimed to investigate physicians' knowledge, attitude, and practice toward AF, to analyze the influencing factors, and to provide data to support departments that develop health policies.Methods: A cross‐sectional study was carried out from October 1, 2016, to March 31, 2018. A standard‐structured anonymous questionnaire was completed by each participant through face‐to‐face interviews.Results: A total of 611 doctors from 38 hospitals were responded to this survey. The mean of the total score of the questionnaire was 21.59 ± 3.559 (total score of the questionnaire was 36), and the mean scores of knowledge, attitude, and practice were 6.86 ± 1.70, 6.13 ± 1.35, and 8.59 ± 2.21, respectively. The doctor' s knowledge, practice scores, and total scores were positively correlated with the education level and the workplace. The influencing factors that affect doctors' knowledge, attitudes, and practice scores including education level, professional title, working years, hospital grade, and hospital location.Conclusions: There was still a big gap in neurologists' knowledge and practice about AF. It is necessary to strengthen the continuous improvement of doctor training to improve the management of AF.