ObjectiveTo review the recent research progress of different types of stem cells in the treatment of ischemic stroke.MethodsBy searching the PubMed database, a systematic review had been carried out for the results of applying different types of stem cells in the treatment of ischemic stroke between 2000 and 2020.ResultsStem cells can be transplanted via intracranial, intravascular, cerebrospinal fluid, and intranasal route in the treatment of ischemic stroke. Paracrine and cell replacement are the two major mechanisms of the therapy. The researches have mainly focused on utilization of neural stem cells, embryonic stem cells, and mesenchymal stem cells. Each has its own advantages and disadvantages in terms of capability of migration, survival rate, and safety. Certain stem cell therapies have completed phase one clinical trial.ConclusionStem cells transplantation is feasible and has a great potential for the treatment of ischemic stroke, albeit that certain obstacles, including the selection of stem cells, transplantation strategy, migration ability, survival rate, still wait to be solved.
Objective To evaluate the predictive effect of three machine learning methods, namely support vector machine (SVM), K-nearest neighbor (KNN) and decision tree, on the daily number of new patients with ischemic stroke in Chengdu. Methods The numbers of daily new ischemic stroke patients from January 1st, 2019 to March 28th, 2021 were extracted from the Third People’s Hospital of Chengdu. The weather and meteorological data and air quality data of Chengdu came from China Weather Network in the same period. Correlation analyses, multinominal logistic regression, and principal component analysis were used to explore the influencing factors for the level of daily number of new ischemic stroke patients in this hospital. Then, using R 4.1.2 software, the data were randomly divided in a ratio of 7∶3 (70% into train set and 30% into validation set), and were respectively used to train and certify the three machine learning methods, SVM, KNN and decision tree, and logistic regression model was used as the benchmark model. F1 score, the area under the receiver operating characteristic curve (AUC) and accuracy of each model were calculated. The data dividing, training and validation were repeated for three times, and the average F1 scores, AUCs and accuracies of the three times were used to compare the prediction effects of the four models. Results According to the accuracies from high to low, the prediction effects of the four models were ranked as SVM (88.9%), logistic regression model (87.5%), decision tree (85.9%), and KNN (85.1%); according to the F1 scores, the models were ranked as SVM (66.9%), KNN (62.7%), decision tree (59.1%), and logistic regression model (57.7%); according to the AUCs, the order from high to low was SVM (88.5%), logistic regression model (87.7%), KNN (84.7%), and decision tree (71.5%). Conclusion The prediction result of SVM is better than the traditional logistic regression model and the other two machine learning models.
Objective To understand the quality of life of patients with acute mild to moderate ischemic stroke one year after stroke, analyze the factors affecting their quality of life, and provide a scientific basis for improving their health-related quality of life. Methods This study included patients who were diagnosed with acute mild to moderate ischemic stroke between March 2019 and March 2021 in four hospitals in Nanchang. Sociodemographic information and relevant clinical data were collected during hospitalization. The EQ-5D-5L questionnaire was administered to assess health-related quality of life one year after discharge. The Mann-Whitney U test (for two groups) and Kruskal-Wallis one-way ANOVA (for multiple groups) were used to analyze differences in utility scores among various factors. A Tobit regression model was built to investigate the factors influencing quality of life one-year post-stroke. Results A total of 1 181 patients participated in the study, including 791 males (66.98%) and 390 females (33.02%), with an average age of 63.7±10.9 years. Health-related quality of life data collected one year after the stroke revealed that 22.69% of patients experienced pain/discomfort, 17.27% suffered anxiety/depression, 15.66% had mobility issues, 10.33% had difficulties with daily activities, and 8.64% had trouble with self-care. Tobit regression results showed that age (β=−0.263, 95%CI −0.327 to −0.198), gender (β=−0.134, 95%CI −0.189 to −0.080), previous hypertension (β=−0.068, 95%CI −0.120 to −0.016), previous dyslipidemia (β=−0.068, 95%CI −0.126 to −0.011), admission NIHSS score (β=−0.158, 95%CI −0.198 to −0.118), and discharge mRS score (β=−0.193, 95%CI −0.250 to −0.136) were negatively associated with health utility values. Current employment status (β=0.141, 95%CI 0.102 to 0.181) and admission GCS score (β=0.209, 95%CI 0.142 to 0.276) were positively correlated with health utility values. Conclusion One year after an acute mild to moderate ischemic stroke, patients commonly face pain/discomfort and anxiety/depression. Factors affecting overall quality of life include age, sex, current employment status, previous hypertension, previous dyslipidemia, admission NIHSS score, admission GCS score, and discharge mRS score. Clinically, developing scientifically sound and reasonable rehabilitation plans post-discharge is crucial for improving long-term quality of life.
Ischemic stroke (IS) is one of the important diseases threatening human health. The occurrence and development of IS can trigger a series of complex pathophysiological changes, including damage to the blood-brain barrier, ion imbalance, oxidative stress, mitochondrial damage, which ultimately lead to the apoptosis and necrosis of nerve cells in the ischemic area. Impaired blood-brain barrier is a key factor for cerebral edema, hemorrhagic transformation and poor prognosis in patients with IS, and neuroinflammatory response plays an important role in the damage and repair of the blood-brain barrier. This article mainly focuses on the neuroinflammatory response mediated by glial cells, pro-inflammatory cytokines and matrix metalloproteinases and the related mechanisms of IS blood-brain barrier damage and repair, in order to provide new directions for the treatment of IS.
Objective To investigate the correlation between stress hyperglycemia ratio (SHR) and acute ischemic stroke (AIS) 1-year prognosis, to provide more clinical basis to improve the prognosis of AIS patients and to target and control the influencing factors. MethodsThe patients with AIS diagnosed for the first time and received treatment at the Shijiazhuang Fifth Hospital between May 2019 and January 2022 were retrospectively and continuously included. According to the Modified Rankin Scale score 1-year after the onset of the disease, the patients were divided into a good prognosis group and a poor prognosis group. Also the patients were divided into 2 groups based on the median of SHR. The correlation between SHR and stress blood glucose was analyzed, and the factors affecting the prognosis of AIS patients were identified. The predictive value of SHR and stress blood glucose on the prognosis of AIS patients was compared using receiver operating characteristic. Results A total of 206 patients were included. Among them, there were 125 cases (60.7%) in the good prognosis group and 81 cases (39.3%) in the poor prognosis group. The median SHR (lower quartile, upper quartile) is 1.20 (1.08, 1.33). There were statistically significant differences between the two groups in the scores of the National Institutes of Health Stroke Scale, diabetes history, hypertension history, low-density lipoprotein cholesterol, stress blood glucose, age, SHR and SHR classification (P<0.05). There was no statistically significant difference in the other indicators compared between the two groups (P＞0.05). Stress blood glucose was positively correlated with SHR (7.95±1.78 vs. 1.21±0.19; r=0.294, P<0.001). Multivariate logistic analysis showed that stress blood glucose and SHR were independent factors influencing the 1-year prognosis of AIS patients (P<0.05), and the interaction between SHR and diabetes was not significant (P>0.05) After adjusting for confounding factors, the area under the receiver operating characteristic curve of SHR for the prognosis of AIS patients was higher than that of stress blood glucose [0.682 (0.614, 0.745) vs .0.585 (0.515, 0.653); Z=2.042, P=0.041]. Conclusions SHR and stress blood glucose are independent risk factors for 1-year prognosis in AIS patients. However, SHR has a better predictive value for 1-year prognosis in AIS patients than stress blood glucose. Whether the patient has diabetes or not, the impact of SHR on the prognosis of AIS patients is consistent.
Diabetes mellitus patients have the characteristics of higher morbidity of ischemic stroke, severe symptoms, more recurrent stroke and higher mortality. Current studies have shown that stroke patients with or without diabetes mellitus have different pathophysiological mechanisms during stroke progress. Accordingly, treatment that is beneficial to non-diabetes mellitus patients may not be beneficial to diabetes mellitus stroke patients. This article reviews the current research status of pathophysiological mechanism of diabetes mellitus complicated with ischemic stroke, and provides reference for the relevant research of drug intervention in diabetes mellitus patients complicated with stroke.
Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it’s difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.
Objective To evaluate the reporting quality of randomized controlled trials (RCTs) on acupuncture for acute ischemic stroke. Methods Six databases including The Cochrane Central Register of Controlled Trials (CENTRAL, Issue 4, 2005), MEDLINE (1966 to December 2005), EMbase (1984 to December 2005), China National Knowledge Infrastructure (CNKI, 1994 to December 2005), China Biomedicine Database disc (CBMdisc, 1980 to December 2005), VIP (a full text issues database of China, 1989 to December 2005) were searched systematically. Handsearch for further references was conducted. Language was limited to Chinese and English. We identified 74 RCTs that used acupuncture as an intervention and assessed the quality of these reports against the Consolidated Standards for Reporting of Trials (CONSORT) statement and Standards for Reporting Interventions in Controlled Trials of Acupuncture (STRICTA).Results In regard to the items in the CONSORT statement, 54 (73%) RCTs described baseline demographic and clinical characteristics in each group. Twenty-six (35%) mentioned the method of generating the random sequence, with 4 (5%) using a computer allocation. Only 6 (8%) RCTs had adequate allocation concealment, with 5 RCTs using sealed opaque envelopes and 1 RCT using centralized computer allocation. Only 8 (11%) RCTs used blinding, including 5 described as double-blind. Four (5%) RCTs reported the sample size calculation and 5 (7%) RCTs reported that an intention-to-treat (ITT) analysis. In regard to the items in STRICTA , only 4 (5%) RCTs reported the numbers of needles inserted. In 35 (47%) RCTs the needle type was reported, but only 26 (35%) mentioned the depths of insertion. Only 1 (1%) RCT mentioned the length of clinical experience and 6 (8%) RCTs reported the background of the acupuncture practitioners, but none stated the duration of their training.Conclusion The reporting quality of RCTs of acupuncture for acute ischemic stroke was low. The CONSORT statement and STRICTA should be used to standardize the reporting of RCTs of acupuncture.
Acute ischemic stroke is the most common type of stroke. Hemorrhagic transformation is one of its serious complications, which may lead to severe neurological deterioration and poor prognosis. The occurrence of hemorrhagic transformation is mainly related to the inflammatory mechanism after infarction, blood-brain barrier injury, ischemia-reperfusion injury and abnormal coagulation function. Identification of early predictors of hemorrhagic transformation can help reduce its incidence and severity. However, the mechanism of hemorrhagic transformation is complex, and there is currently no unified standard for its prediction. This article aims to review the related mechanisms and early predictors of hemorrhagic transformation after stroke, in order to provide a reference for early identification and prevention.
Objective To explore the efficacy of endovascular therapy in elderly patients with acute ischemic stroke. Methods The acute ischemic stroke patients who received endovascular therapy between January 2020 and January 2023 were retrospectively enrolled. According to age, patients were divided into the elderly group (≥ 80 years old) and other age groups (<80 years old). The baseline data, green channel data, nerve function deficit, recanalization and complication information were collected, and the patients were followed up. Modified Rankin Scale (mRS) was used to evaluate patients prognosis at 3 months after onset. Score less than or equal to 2 points was defined as good prognosis and over 2 points was defined as poor prognosis. Results A total of 138 patients were included, and 7 patients were lost to follow-up. Finally, 131 patients were included. Among them, there were 50 cases in the elderly group and 81 cases in the other age group. There were statistically significant differences in age, hypertension, atrial fibrillation, and vascular recanalization between the elderly group and the other age group (P<0.05). There was no statistically significant difference in the other baseline data, complications, 3-month prognosis, or mortality between the two groups (P>0.05). The results of multivariate logistic regression analysis showed that the National Institute of Health Stroke Scale score at admission [odds ratio (OR)=1.150, 95% confidence interval (CI) (1.033, 1.281), P=0.011], pulmonary infection [OR=2.933, 95%CI (1.109, 7.758), P=0.030], and hypoproteinemia [OR=3.716, 95%CI (1.226, 11.264), P=0.020] affected the mRS score at 3 months after onset. Conclusions Among the patients with acute ischemic stroke undergoing endovascular therapy, there is no difference in the occurrence of complications or short-term prognosis between elderly patients and other age patients. However, the attention should still be paid to reducing the occurrence of complications in patients, strengthening their nutritional support, and thereby improving their prognosis.