Lulu Pei

Acute ischemic stroke (AIS) induces high level of neutrophils, which correlates inversely with patient survival. Pyroptosis induced by gasdermin D (GSDMD) has been shown to have an important role in the pathophysiology of several inflammatory disorders. The role of GSDMD in the high level of neutrophils after AIS is unknown. Using a middle cerebral artery occlusion (MCAO) mouse model, we identified activation of pyroptosis signal, including expression of caspase-1/11, GSDMD, and interleukin-1β/18 (IL-1β/18), in the brain and spleen at early ischemic injury. Knockout of GSDMD in mice reduced infarct size, improved neurological function, and increased survival after MCAO. GSDMD deficiency decreased the overall degree of inflammation and the proportion of neutrophils in the brain after MCAO. Quantitative studies of neutrophils at several time intervals and organs demonstrated that early inflammatory leucocyte production and supplement (1 day after MCAO) was GSDMD-dependent. A series of bone marrow transplantation experiments, neutrophil depletion experiments, and RNA sequencing results demonstrated that neutrophil specific GSDMD is essential for the production and supply of neutrophil in bone marrow to blood. Moreover, pharmacological suppression of GSDMD decreased pathological abnormalities, infarct volume, and ameliorated neurological function. These results provided a new viewpoint on the immunological modulation of neutrophils after MCAO and suggest that suppression of GSDMD may relieve the neuroinflammatory load, thereby providing a potential treatment strategy for stroke.

It is essential to identify high risk transient ischemic attack (TIA) patients. The previous study reported that the CSR (comprehensive stroke recurrence) model, a neuroimaging model, had a high predictive ability of recurrent stroke. The aims of this study were to validate the predictive value of CSR model in TIA patients and compare the predictive ability with ABCD³-I score. Data were analyzed from the prospective hospital-based database of patients with TIA which defined by the World Health Organization time-based criteria. The predictive outcome was stroke occurrence at 90 days. The receiver-operating characteristic (ROC) curves were plotted and the C statistics were calculated as a measure of predictive ability. Among 1186 eligible patients, the mean age was 57.28 ± 12.17 years, and 474 (40.0%) patients had positive diffusion-weighted imaging (DWI). There were 118 (9.9%) patients who had stroke within 90 days. In 1186 TIA patients, The C statistic of CSR model (0.754; 95% confidence interval [CI] 0.729–0.778) was similar with that of ABCD³-I score (0.717; 95% CI 0.691–0.743; Z = 1.400; P = 0.1616). In 474 TIA patients with positive DWI, C statistic of CSR model (0.725; 95% CI 0.683–0.765) was statistically higher than that of ABCD³-I score (0.626; 95% CI 0.581–0.670; Z = 2.294; P = 0.0245). The CSR model had good predictive value for assessing stroke risk after TIA, and it had a higher predictive value than ABCD³-I score for assessing stroke risk for TIA patients with positive DWI.

Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has great significance on prognosis prediction for stroke patients. In this paper, we present an interpretable deep learning model to predict the one-year mortality risk on stroke. We design sub-modules to reconstruct features from original clinical data that highlight the dissimilarity and temporality of different variables. The model consists of Bidirectional Long Short-Term Memory (Bi-LSTM), in which a novel correlation attention module is proposed that takes the correlation of variables into consideration. In experiments, datasets are collected clinically from the department of neurology in a local AAA hospital. It consists of 2,275 stroke patients hospitalized in the department of neurology from 2014 to 2016. Our model achieves a precision of 0.9414, a recall of 0.9502 and an F1-score of 0.9415. In addition, we provide the analysis of the interpretability by visualizations with reference to clinical professional guidelines.

Objective: The aim of this investigation was to examine the association between the serum homocysteine (Hcy) level and the distal single small subcortical infarction (dSSSI). Methods: Consecutive patients were prospectively recruited in a registered hospital-based ischemic stroke database. Baseline characteristics and risk factors of single small subcortical infarction (SSSI), including the serum Hcy level, were assessed. The SSSI located in the lenticulostriate artery (LSA) territory was divided into proximal single small subcortical infarction (pSSSI) and dSSSI based on a standard template on axial diffusion-weighted imaging (DWI). The association between the serum Hcy level and dSSSI was analysed by multivariate logistic regression analysis. Results: Out of 3,247 patients, a total of 572 patients were included in the final analysis. We found that dSSSI had a higher serum Hcy level than pSSSI. Elevated Hcy level was independently correlated with dSSSI. Compared with the lowest quartile, the upper quartiles of Hcy level were independently associated with dSSSI, the odds ratio for the second quartile was 1.748 (95%CI 1.019 to 3.000), 1.824 (95% CI 1.060 to 3.140) for the third quartile, and 2.010 (95% CI 1.155 to 3.497) for the fourth quartile. The restricted cubic spline showed that the higher level of Hcy, the greater risk of developing dSSSI. Conclusion: The dSSSI shows higher serum Hcy level than pSSSI. Elevated serum Hcy is more closely related to dSSSI. In the future, the effectiveness of Hcy-lowering therapy for dSSSI needs to be explored by further clinical studies.