Table of Content

20 April 2025, Volume 20 Issue 4

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Digital Biomarkers: Unlocking the Potential of Digital Intelligent Healthcare

ZHOU Hongyu, LI Zixiao

2025, 20(4):  385-390.  DOI: 10.3969/j.issn.1673-5765.2025.04.001

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Digital biomarkers are a new index constructed by utilizing digital intelligence technology to actively or passively collect individuals’ behavioral, physiological, or biochemical data through consumer-level products such as wearable devices, and subsequently processed using artificial intelligence-based algorithms. In recent years, the research on digital biomarkers has developed rapidly in fields such as diabetes mellitus, cardiovascular disease, epilepsy, Parkinson’s disease, and depression, but its application in cerebrovascular disease is still in the early stages. This article compares the differences between traditional and digital biomarkers, reviews commonly used digital devices types, discusses the screening, applications, and challenges of digital biomarkers, and analyzes their potential in conjunction with the characteristics of cerebrovascular disease, aiming to provide new insights for advancing personalized medicine, drug development, and brain-computer interface techniques.

Digital Biomarkers

LI Zixiao, LIU Tao

2025, 20(4):  391-391. 

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A Machine Learning Model Based on Brain Imaging and Clinical Features for Predicting Atrial Fibrillation Detected after Stroke

ZHANG Liyuan, LIU Tao, JIANG Yong, LI Zixiao, WANG Yongjun, YANG Xiaomeng

2025, 20(4):  392-400.  DOI: 10.3969/j.issn.1673-5765.2025.04.002

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Objective  To investigate the predictive value of a machine learning model based on brain imaging and clinical features in patients with atrial fibrillation detected after stroke. 
Methods  A retrospective cohort design was used in this study. Data was derived from the ischemic stroke and TIA patients enrolled in the China national stroke registry Ⅲ from August 2015 to March 2018. Patients were divided into two groups according to the systematic collection of past medical history records, electrocardiogram, and 24-hour Holter monitoring results during hospitalization: the sinus rhythm group and the atrial fibrillation detected after stroke group. Firstly, a pre-trained nnUNet deep learning framework was applied for standardized preprocessing and automated lesion segmentation of DWI data. Subsequently, 960 quantitative imaging features encompassing eight categories, including morphological characteristics, first-order statistics, and advanced texture features, were extracted using the PyRadiomics open-source package. During the feature engineering stage, the Spearman’s rank correlation coefficient analysis was applied (preset threshold |ρ|>0.8) to eliminate highly collinear features. After retaining independent features, the least absolute shrinkage and selection operator (LASSO) regression algorithm was used for feature selection and to construct a joint prediction model. The model performance was internally validated via five-fold cross-validation, and the AUC of the ROC curve was used as the primary evaluation indicator. Finally, the SHapley Additive exPlanations framework was used to analyze the importance of features. 
Results  A total of 1464 ischemic stroke patients were included, with an average age of (64.5±11.1) years, including 498 patients with atrial fibrillation detected after stroke and 966 patients with sinus rhythm. The average AUC of five-fold cross-validation of the prediction model for atrial fibrillation detected after stroke constructed using 15 clinical features was 0.71 (95%CI 0.67-0.74). Clinical and imaging features were fused to form 975 multimodal features, with an average AUC of 0.73 (95%CI 0.70-0.76). Using the LASSO algorithm for feature selection, 31 multimodal features (including 25 imaging and 6 clinical features) were obtained after screening, with an average AUC of 0.73 (95%CI 0.70-0.77). 
Conclusions  The machine learning model based on brain imaging and clinical features can effectively predict atrial fibrillation detected after stroke, and can be further applied in clinical practice.




Construction of an Artificial Intelligence Upper Limb Multi-Joint Motion State Recognition System Based on IMU Signals—A Preliminary Study for the Development of an Artificial Intelligence Motor Function Assessment and Detection System after Stroke

CHENG Xiangxin, ZHANG Shuo, DU Songjun, LIU Ziyang, ZHOU Hongyu, JIA Weili, LI Zixiao, LIU Tao

2025, 20(4):  401-409.  DOI: 10.3969/j.issn.1673-5765.2025.04.003

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Objective  This study aims to develop and construct an upper limb multi-joint motion state recognition system based on low-cost inertial measurement unit (IMU) signals, for the rapid and reliable decoding of multi-joint (forearm, elbow joint, and shoulder joint) motion states in human activities, providing support for motion pattern recognition and daily motion monitoring in post-stroke upper limb rehabilitation assessment.
Methods  This study enrolled four healthy subjects, collecting their six-dimensional (triaxial acceleration+triaxial angular velocity) motion signals through IMUs deployed on the wrist and upper arm, each subject repeating 10 times. Based on the flexor synergy movement in the Fugl-Meyer motor assessment-upper extremity, eight subtasks were designed, each corresponding to a ternary state label of the forearm (supinated or not), elbow joint (flexed or not), and shoulder joint (elevated or not). A multi-label classification framework based on single-task migration (i.e., independently training single-joint classifiers and then merging the outputs) was constructed. At the algorithm level, traditional machine learning methods (time-frequency domain features+random forest) were compared with deep learning algorithms (long short-term memory-based end-to-end learning). Five-fold cross-validation was used to evaluate the accuracy of the upper limb multi-joint motion state recognition system, and ablation experiments were designed to analyze the impact of sensor configuration (e.g., wrist-only vs. wrist+arm) on decoding performance, exploring hardware optimization potential.
Results  A total of 320 motion data samples were collected from four healthy subjects in this study. The results demonstrated that the motion state recognition system designed in this study performed well in multi-joint state decoding of the upper limb. The average accuracy of elbow joint state classification by the traditional machine learning methods was 79.37%, while the deep learning model IBNet reached 87.5%, indicating a stronger pattern-learning capability. The ablation experiment showed that the accuracy of elbow joint state classification exceeded that of dual IMU configuration (92.5% vs. 87.5%) when wrist IMU was used only, and the difference was not significant in other tasks. This suggested that optimizing sensor deployment (e.g., reducing upper arm IMUs) can reduce system complexity while maintaining high performance.
Conclusions  This study successfully constructed a low-cost IMU-based upper limb motion state recognition system. The results showed that deep learning algorithms were superior to traditional machine learning in decoding complex motion patterns, and a single-wrist IMU could replace the dual-sensor configuration in specific tasks, providing a basis for hardware optimization.




Unsupervised Learning-Based Identification of Responders to Indobufen Treatment in Acute Moderate-to-Severe Ischemic Stroke

PU Shanyu, PAN Yuesong, WANG Yongjun

2025, 20(4):  410-417.  DOI: 10.3969/j.issn.1673-5765.2025.04.004

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Objective  To identify responders with non-inferior efficacy of indobufen compared to aspirin in reducing the risk of stroke recurrence based on unsupervised learning method.
Methods  Based on the indobufen versus aspirin in acute ischemic stroke (INSURE) study, an unsupervised learning framework constructed using multiple kernel learning for dimensionality reduction and K-means clustering was developed to identify patients whose primary outcomes of indobufen were non-inferior to aspirin in treating acute moderate-to-severe ischemic stroke (i.e., responders). In the identified patients, the risks of secondary efficacy outcomes (new stroke within 1 year, new complex vascular events within 3 months and 1 year, new ischemic stroke within 3 months and 1 year, and adverse functional outcomes within 3 months and 1 year), safety outcomes (moderate-to-severe bleeding within 3 months), and secondary safety outcomes (moderate-to-severe bleeding within 1 year, any bleeding, and death within 3 months and 1 year) were evaluated for indobufen compared to aspirin. Differences of baseline data between responders and non-responders were compared.
Results  A total of 931 indobufen responders were identified (HR 0.70, 95%CI 0.46-1.06, Pnon-inferiority=0.004). Among the responders, the risks of secondary efficacy outcomes were lower in the indobufen group than in the aspirin group (new stroke within 1 year: HR 0.63, P=0.013; new ischemic stroke within 1 year: HR 0.68, P=0.039; new complex vascular events within 1 year: HR 0.64, P=0.014). The risk of any bleeding within 1 year was lower in the indobufen group than in the aspirin group (HR 0.41, P=0.025). The proportions of females, intracranial atherosclerotic stenosis, hypertension, diabetes mellitus, and dyslipidemia in the responders were significantly higher than in the non-responders. 
Conclusions  Unsupervised learning can be used to identify the responders with non-inferior efficacy of indobufen compared to aspirin in reducing the recurrence risk of  stroke. The proportions of females, intracranial atherosclerotic stenosis, and metabolic disorders in responders were higher than those in non-responders.




Risk Factors Analysis and Prediction Model Development for Poor Prognosis after Endovascular Treatment of Middle Cerebral Artery Occlusive Acute Ischemic Stroke

JIANG Lan, FU Xinmin, SUN Mengfei, LI Yiping

2025, 20(4):  418-427.  DOI: 10.3969/j.issn.1673-5765.2025.04.005

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Objective  To explore the risk factors associated with poor short-term prognosis of patients with middle cerebral artery occlusive acute ischemic stroke (MCAO-AIS) undergoing endovascular treatment and to develop a prediction model. 
Methods  Clinical, imaging, surgical data, and postoperative laboratory examination results of MCAO-AIS patients who underwent endovascular treatment at Xuzhou Central Hospital from February 2022 to May 2024 were retrospectively analyzed. Patients were divided into the good prognosis group and the poor prognosis group based on their mRS score on postoperative day 90. Factors influencing poor prognosis were explored by univariate analysis, collinearity analysis, and multivariate logistic regression analysis, and the prediction model was constructed. The predictive performance of the model was assessed and validated using the ROC curve, calibration curve, and goodness-of-fit test. 
Results  A total of 245 patients were included, with 91 in the good prognosis group and 154 in the poor prognosis group. Multivariate logistic regression analysis showed that higher NIHSS scores at the onset (OR 1.017, 95%CI 1.026-1.119, P=0.002), longer duration of surgery (OR 1.014, 95%CI 1.004-1.023, P=0.004), postoperative cerebral edema (OR 11.396, 95%CI 4.884-26.591, P<0.001), postoperative pneumonia (OR 5.609, 95%CI 2.179-14.436, P<0.001), higher levels of fibrinogen (FIB) (OR 1.877, 95%CI 1.214-2.903, P=0.005), and higher levels of S100 calcium binding protein β (S100β) (OR 1.013, 95%CI 1.004-1.022, P=0.003) were independent risk factors for poor prognosis, while the use of mechanical thrombectomy using stent retriever combined with aspiration under intracranial support catheter assistance (SWIM) combined with angioplasty (OR 0.140, 95%CI 0.045-0.429, P=0.001) was an independent protective factor. ROC curve analysis showed that the AUC of the model constructed based on the seven independent predictor variables determined by multivariate logistic regression analysis was 0.934 (95%CI 0.905-0.964), which had a good predictive efficacy. The sensitivity of the model was 0.844 and the specificity was 0.890 when the optimal cutoff value was 0.665. The Hosmer-Lemeshow test showed that the prediction model was well calibrated, and the calibration plot showed that the model prediction curve and the actual curve had a high degree of fitting. 
Conclusions  The model constructed based on the NIHSS score at the onset, operative time, postoperative cerebral edema, postoperative pneumonia, FIB, S100β, and SWIM combined with angioplasty has a good predictive efficacy for poor prognosis on day 90 after endovascular treatment in patients with MCAO-AIS.


Construction and Evaluation of Pre-Hospital Delay Risk Prediction Model for Acute Ischemic Stroke

NI Dianli, CHEN Xiaobing, ZHANG Guanghui, PENG Qingrong

2025, 20(4):  428-434.  DOI: 10.3969/j.issn.1673-5765.2025.04.006

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Objective  To analyze the influencing factors of pre-hospital delay in acute ischemic stroke (AIS) and construct a risk prediction model. 
Methods  AIS patients admitted to The First People’s Hospital of Lianyungang from March 2021 to January 2024 were selected as research subjects. They were divided into the model group and the internal validation group. Based on pre-hospital delay conditions, the model group was further divided into the non-delayed (<3.5 h) group and the delayed (≥3.5 h) group. The data from the model group was used to establish a nomogram model, and the data from the internal validation group was used to assess the model’s generalization performance. Multivariate logistic regression was used to analyze the influencing factors of pre-hospital delay in AIS patients. The rms package in R software was used to construct a nomogram prediction model for the pre-hospital delay of AIS patients. The ROC curve, calibration curve, and Hosmer-Lemeshow test were used to evaluate the predictive efficacy of the model, and the clinical decision curve was used to evaluate its clinical application value. 
Results  A total of 268 patients with AIS were enrolled, among whom 141 (52.61%) had pre-hospital delay. In patients of the model group, the delayed group was older on average (P<0.001) and had significantly higher proportions of the following characteristics: living in rural areas (P=0.004), nocturnal onset (P=0.018), without disturbance of consciousness (P=0.004), and not receiving disease knowledge education (P=0.001) compared with the non-delayed group. Multivariate logistic regression analysis showed that advanced age (OR 1.082, 95%CI 1.038-1.128, P<0.001), living in rural areas (OR 3.201, 95%CI 1.402-7.307, P=0.006), nocturnal onset (OR 6.873, 95%CI 2.809-16.815, P<0.001), without disturbance of consciousness (OR 4.599, 95%CI 1.934-10.940, P=0.001), and not receiving disease knowledge education (OR 4.134, 95%CI 1.927-8.866, P<0.001) were independent risk factors for pre-hospital delay in AIS patients. Based on these factors, a nomogram model was developed, with higher total scores indicating an increased risk of pre-hospital delay. The ROC curve showed that the AUC of the model’s prediction of pre-hospital delay was 0.822 (95%CI 0.763-0.880) in the model group and 0.844 (95%CI 0.755-0.932) in the internal validation group. In addition, both groups of data passed the Hosmer-Lemeshow test. The calibration curve showed that the predicted value of the model was relatively consistent with the true value. The clinical decision curve showed that the clinical application value of the model was reasonable.
Conclusions  The risk of pre-hospital delay of AIS patients is influenced by age, residence, time of onset, presence or absence of disturbance of consciousness, and whether they received disease knowledge education. The model constructed on this basis has good predictive efficacy.


Correlation between Cognitive Load and Physiological Indicators in Motor Imagery Training in Patients with Acute Ischemic Stroke

ZHANG Xinyue, CHANG Hong, ZHAO Jie, LI Peipei, LIU Mengrao, LI Suai

2025, 20(4):  435-446.  DOI: 10.3969/j.issn.1673-5765.2025.04.007

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Objective  To explore the relationship between cognitive load and different physiological indicators by dynamic monitoring the physiological indicators in the motor imagery training in patients with acute ischemic stroke. 
Methods  A descriptive correlational research design was adopted, and patients with acute ischemic stroke who met the inclusion criteria were consecutively enrolled for assessments from February to November 2022 at the Department of Neurology of Xuanwu Hospital, Capital Medical University. Patients mimicked the grasping actions on the computer screen while holding a ball with both hands. They first performed left-hand grasping imagery training (4 seconds) three times, then rested for 3 seconds. After that, they performed right-hand grasping imagery training (4 seconds) three times, then rested for 3 seconds again. A total of 42 rounds and 252 grasping imagery training sessions were conducted, with a total duration of approximately 20 minutes. Before the start of motor imagery training and immediately after its completion, the cognitive load scale was used to measure the cognitive load, and the physiological indicators were monitored throughout the training. The relationship between cognitive load and physiological indicators was analyzed using latent profile and correlation analyses. 
Results  A total of 102 patients with acute ischemic stroke were included, with an average age of (58.94±13.07) years, the time from onset to treatment of 2 (1-5) days, and the NIHSS score of 3 (2-5) points. Results from latent profile and correlation analyses revealed  that the latent types of cognitive load in patients with acute ischemic stroke before motor imagery training were divided into three classes, specifically “low cognitive load in all dimensions type” “high effort and zero physical requirements type” and “high mental requirements-self-expression type”. Heart rate, pulse rate, respiratory rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, coefficient of variability of systolic blood pressure, coefficient of variability of diastolic blood pressure, heart rate immediately after the end, pulse rate immediately after the end, respiratory rate immediately after the end, and blood oxygen saturation immediately after the end were correlated with the total score of cognitive load and various dimensions of cognitive load. The latent types of cognitive load during motor imagery training were divided into four classes, specifically “high time limit requirements-low frustration type” “high physical requirements-time limit requirements-effort level-frustration and low self-expression type” “high effort level and low self-expression-frustration type” and “high physical requirements-effort level-frustration and low self-expression type”. Heart rate, pulse rate, respiratory rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, mean arterial pressure, coefficient of variability of systolic blood pressure, coefficient of variability of diastolic blood pressure, heart rate immediately after the end, pulse rate immediately after the end, respiratory rate immediately after the end, blood oxygen saturation immediately after the end, systolic blood pressure immediately after the end, diastolic blood pressure immediately after the end, and mean arterial pressure immediately after the end were correlated with the total score of cognitive load and various dimensions of cognitive load. The results of the canonical correlation analysis between cognitive load and vital signs showed that among classes one to four in motor imagery training, the first canonical correlation pair in class one was statistically significant (P<0.001), with a canonical correlation coefficient＞0.999. The absolute values of the standardized coefficients for systolic and diastolic blood pressure variability were relatively high.
Conclusions  In patients with acute ischemic stroke, there were correlations between cognitive load and vital signs across latent profile classes before and during motor imagery training. Notably, the coefficients of variability of systolic and diastolic blood pressure indicated cognitive load of patients in class one during training more accurately.


Trends and Predictive Analysis of the Burden of Stroke Deaths Attributable to High LDL-C in China from 1990 to 2021

ZHANG Yongqing, LI Na, GAO Yili, QIN Jiawen, YU Haiping, ZHAO Tingting

2025, 20(4):  447-456.  DOI: 10.3969/j.issn.1673-5765.2025.04.008

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Objective  To analyze the trends in the burden of stroke deaths attributable to high LDL-C in China from 1990 to 2021, explore the effects of age, period, and cohort factors, and predict the future changes in the burden of death.
Methods  Using the data from the global burden of disease database 2021 on stroke deaths attributable to high LDL-C in Chinese adults aged≥20 years from 1990 to 2021, the Joinpoint regression model was used to analyze the trends of age-standardized death rates over time. Through the age-period-cohort model, the age, period, and cohort effects of stroke deaths attributable to high LDL-C in China were estimated. The autoregressive integrated moving average (ARIMA) model was used to predict the burden of stroke deaths attributable to high LDL-C in China from 2022 to 2030.
Results  Compared with 1990, the number and rate of stroke deaths attributable to high LDL-C in China have significantly increased in 2021, while the age-standardized death rates has decreased. In 2021, the total number of deaths for the whole population, males, and females were 300 100, 170 500, and 129 500, respectively, which were 161.87%, 187.04%, and 134.60% higher than those in 1990. The mortality rates in 2021 were 21.08/100 000, 23.42/100 000, and 18.64/100 000, respectively, which were 116.65%, 139.22%, and 92.56% higher than those in 1990. The age-standardized death rates in 2021 were 15.93/100 000, 20.96/100 000, and 12.36/100 000, with estimated annual percentage changes of -0.43%, -0.02%, and -0.88%, respectively (all P<0.05). The results of the Joinpoint regression model showed that from 1990 to 2021, the age-standardized death rates of stroke attributable to high LDL-C in China showed a trend of first increasing and then decreasing in general, and the age-standardized death rates were higher in males than in females. The results of the age-period-cohort model analysis showed that the stroke mortality rates attributable to high LDL-C generally increased with age, with RR ranging from 0.028 to 12.214 in the whole population, 0.027 to 14.661 in males, and 0.032 to 15.258 in females. The period effect showed an increasing trend, with RR ranging from 0.677 to 1.418 in the whole population, 0.623 to 1.532 in males, and 0.750 to 1.298 in females. The cohort effect showed a decreasing trend, with RR ranging from 0.213 to 4.210 in the whole population, 0.221 to 3.637 in males, and 0.170 to 3.978 in females. The results of the ARIMA model predicted that from 2022 to 2030, the number and rate of stroke deaths attributable to high LDL-C in China’s whole population, males, and females will continue to rise. The age-standardized death rates for males will show an increasing trend, but the age-standardized death rates for the whole population and females will remain steady.
Conclusions  From 1990 to 2021, the burden of stroke deaths attributable to high LDL-C has been increasing in China, and the burden of death is heavier in males than in females. Mortality increases with age, grows over periods, and decreases by birth cohorts. The burden of death is expected to remain heavy for the foreseeable future.




Effects of the Ratio of Extracellular Water to Total Body Water on Discharge Outcomes in Patients with Acute Ischemic Stroke

WANG Yan, HAO Huaiyu, LU Qiang, ZHANG Lei, SHEN Xueyan, WEI Guimei

2025, 20(4):  457-461.  DOI: 10.3969/j.issn.1673-5765.2025.04.009

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Objective  To investigate the effects of the ratio of extracellular water to total body water (ETR) on discharge outcomes in patients with acute ischemic stroke (AIS).
Methods  Patients with AIS who were hospitalized in the Neurology Department, Liangxiang Hospital of Beijing Fangshan District, from July 2018 to June 2024 were retrospectively analyzed. They were divided into two groups based on their discharge outcomes: the good outcome (mRS score＜3 points) group and the poor outcome (mRS score≥3 points) group. Differences in clinical data and indicators like ETR were compared between the two groups. Multivariate logistic regression analysis was used to explore the effect of ETR on patient discharge outcomes. Furthermore, subgroup analyses were conducted to evaluate ETR’s effect on discharge outcomes by sex, age, BMI, skeletal muscle index, and admission NIHSS score. 
Results  A total of 147 AIS patients were included, with an average age of (68.9±12.2) years, including 80 males (54.4%), 107 cases (72.8%) in the good outcome group, and 40 cases (27.2%) in the poor outcome group. Multivariate logistic regression analysis showed that increased ETR (OR 2.09, 95%CI 1.16-3.76, P=0.014) and ETR≥40.0% (OR 6.24, 95%CI 1.33-29.32, P=0.020) were risk factors for poor discharge outcomes in patients with AIS. The subgroup analysis results remained robust with no significant interaction (Pinteraction＞0.05). In the subgroup of patients with age<71 years, BMI≥24 kg/m2, and admission NIHSS score≥3 points, the effect of ETR on poor discharge outcomes was significant.
Conclusions  ETR is an independent factor influencing discharge outcomes in patients with AIS, and high ETR can significantly increase the risk of poor discharge outcomes.


Meta-Analysis of the Efficacy and Safety of Intravenous Ginkgo Biloba Leaf Preparation in the Treatment of Acute Ischemic Stroke

HU Yanqin, ZHAO Taoli, LI Shen, GUO Dongxing, ZHAO Zhigang

2025, 20(4):  462-469.  DOI: 10.3969/j.issn.1673-5765.2025.04.010

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Objective  To systematically evaluate the efficacy and safety of intravenous ginkgo biloba leaf preparation in the treatment of acute ischemic stroke (AIS).
Methods  Randomized controlled trials of intravenous ginkgo biloba leaf preparation in the treatment of AIS in English databases such as PubMed, Embase, and Cochrane Library, as well as in Chinese databases like Wanfang, CNKI, and VIP were searched by computer. The search period was from the establishment of these databases to July 30, 2024. The quality of the included literature was evaluated using the Cochrane RevMan bias risk assessment table. Using RevMan 5.3 software, a meta-analysis was performed on prognosis, early neurological improvement, neurological deficits, total effective rate, adverse reactions, and other indicators of ginkgo biloba leaf preparation in the treatment of AIS. 
Results  A total of 9 articles were included in this study, including 3 in English and 6 in Chinese, which involved 6111 patients, comprising 3103 in the control group and 3008 in the experimental group. The meta-analysis results showed that intravenous ginkgo biloba leaf preparation could improve the rate of good prognosis (mRS score≤2 points) of AIS patients (OR 1.95, 95%CI 1.72-2.22, P＜0.001), reduce the mRS score [standardized mean difference (SMD) －0.67, 95%CI －0.76-－0.58, P＜0.001], enhance the rate of early neurological improvement of patients (OR 1.23, 95%CI 1.07-1.41, P=0.003), and decrease the NIHSS score of patients (SMD －1.36, 95%CI －1.54-－1.17, P＜0.001). Ginkgo biloba leaf preparation could improve the total effective rate of AIS treatment (OR 3.68, 95%CI 1.81-7.49, P＜0.001), without increasing the incidence of adverse reactions (OR 1.01, 95%CI 0.85-1.20, P=0.870).
Conclusions  Intravenous ginkgo biloba leaf preparation has a brain cytoprotection effect on AIS and can improve the prognosis of patients without increasing adverse reactions.


Interpretation of the 2024 International Consensus on Optic Nerve Sheath Diameter Ultrasound Imaging and Measurement

TIAN Bing, ZHOU Fubo, WANG Lijuan, XING Yingqi

2025, 20(4):  470-478.  DOI: 10.3969/j.issn.1673-5765.2025.04.011

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Ultrasound imaging and measurement of the optic nerve sheath diameter (ONSD) have long been a topic of significant interest among clinicians and radiologists. In 2024, experts in ONSD ultrasound from multiple countries jointly established an international consensus on ONSD ultrasound imaging and measurement, published in Critical Care Medicine. This consensus introduced the ONSD point-of-care ultrasonography quality criteria checklist, providing standardized recommendations for ONSD ultrasound imaging and measurement. It also provided explanations on the technical details and training suggestions for ONSD measurement in subsequent related research. This article interprets this expert consensus.



Research Progress on the Role of cGAS-STING Pathway and Syntaxin 17 Mediated Autophagy in Cerebral Ischemia-Reperfusion Injury

YANG Hang, GAO Anbang, NI Ying, MA Yue, GAO Mingtong

2025, 20(4):  479-485.  DOI: 10.3969/j.issn.1673-5765.2025.04.012

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After acute ischemic stroke, cerebral ischemia-reperfusion injury (CIRI) can further damage the neurological function of patients and affect their prognosis. Autophagy is a “double-edged sword” in the pathological process of CIRI. The varying stages and degrees of activation of CIRI can have opposing effects, either protecting or damaging brain tissue. The cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS)-stimulator of interferon gene (STING) pathway is an important pattern recognition and effector pathway in the innate immune response. Syntaxin 17 (STX17) is a member of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor subfamily. Both play significant regulatory roles in autophagy and mitophagy during the CIRI process. This article reviews the mechanisms and interrelationships between the cGAS-STING pathway and STX17 in regulating autophagy during CIRI, aiming to provide new ideas and evidence for the intervention of CIRI.

Research Progress on the Mechanism of Acupuncture in Treating Ischemic Stroke Based on the Gut-Brain Axis

ZHAO Qiankun, LI Xiandong, XU Tiance, CHEN Huisheng

2025, 20(4):  486-492.  DOI: 10.3969/j.issn.1673-5765.2025.04.013

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Acupuncture, as an important part of traditional Chinese medicine, has received widespread attention in recent years for its mechanisms in the treatment of ischemic stroke. This paper summarizes and analyzes the basic research on acupuncture treating ischemic stroke through modulation of the gut-brain axis. It is found that it may involve the following mechanisms. Firstly, it modulates the gut microbiota composition by inhibiting the proliferation of pathogenic bacteria and promoting the colonization of beneficial bacteria. It also enhances the expression of intestinal epithelial tight junction protein to maintain gut barrier function and reduce enterotoxin entry into the bloodstream. Secondly, it inhibits the overactivation of the hypothalamic-pituitary-adrenal axis through neuroendocrine regulation, reduces the release of pro-inflammatory cytokine, and activates the anti-inflammatory pathways mediated by the vagus nerve. Thirdly, it regulates the dynamic balance of gut microbiota metabolic products such as short-chain fatty acids and trimethylamine N-oxide, ameliorating the systemic inflammatory microenvironments. Fourthly, it reshapes the proportion of immune cell subpopulations, inhibits excessive inflammation reactions, and facilitates neural repair. By synthesizing recent research advancements, this paper reviews potential therapeutic targets and pathways through which acupuncture modulates ischemic stroke via the gut-brain axis, aiming to provide a basis for subsequent basic research and clinical application of acupuncture.

Research Progress in Assessing the Onset Time of Acute Ischemic Stroke

CHI Qi, ZHAO Ying, HAN Dongqian, ZHANG Siqi, DU Peijie, DONG Wanyue, XU Anding, YANG Zhenguo, MENG Heng

2025, 20(4):  493-499.  DOI: 10.3969/j.issn.1673-5765.2025.04.014

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The focus of acute ischemic stroke treatment in the hyperacute phase is reperfusion therapy, which is strictly time-dependent. However, patients with wake-up stroke face challenges in reperfusion therapy decisions due to the unclear onset time of the disease. This article reviews the research progress in assessing the onset time of acute ischemic stroke patients, covering assessment methods based on circadian rhythms, imaging, and artificial intelligence. It analyzes the efficacy of different methods in assessing the onset time of acute ischemic stroke patients, aiming to apply these findings to wake-up stroke patients, providing a supportive basis for the selection of their reperfusion therapy.

Advances in Cerebral Collateral Circulation Assessment Methods

ZHAO Kaijie, SUN Xiuting, YUAN Man, HU Wanzhen, ZHANG Xiaoyan

2025, 20(4):  500-510.  DOI: 10.3969/j.issn.1673-5765.2025.04.015

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Collateral circulation holds significant clinical value in the prognostic evaluation of patients with acute ischemic stroke. By establishing hemodynamic compensatory pathways, it effectively reduces the risk of neuronal injury in ischemic core regions, especially demonstrating irreplaceable neuroprotective effects in cases of large vessel stenosis or occlusion. Precise assessment of collateral circulation can not only guide the selection of indications for revascularization therapy and optimize the determination of treatment time window but also significantly influence the procedural success rates of mechanical thrombectomy and the risks of postoperative cerebral hyperperfusion syndrome. Single-parameter assessment of the collateral circulation has limitations, and the latest evidence-based medical evidence supports the construction of a multimodal comprehensive assessment system. This paper systematically reviews four aspects of collateral circulation assessment, including conventional imaging assessment methods, venous outflow hemodynamic parameter analysis, biomarker detection, and artificial intelligence-assisted multimodal CT imaging techniques. The integrated application of these assessment methods provides clinicians with multidimensional means of assessing collateral circulation, offering crucial technical support for achieving precise and individualized stroke treatment.

Development and Reliability and Validity Testing of the Intravenous Thrombolysis Intention Scale for Acute Ischemic Stroke

GUO Yuanli, GAO Renke, YANG Caixia, FAN Wenfeng, GUO Li’na, DONG Xiaofang, LYU Peihua, GAO Huanhuan, MA Keke

2025, 20(4):  511-518.  DOI: 10.3969/j.issn.1673-5765.2025.04.016

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Objective  To develop an intravenous thrombolysis intention scale for acute ischemic stroke and evaluate its reliability and validity. 
Methods  This study used the theory of planned behavior as a guiding framework, forming an initial draft of the scale through literature analysis, Delphi expert consultation, and pre-survey. From July to August 2023, a questionnaire survey was conducted at three screening sites for high-risk stroke populations in Zhengzhou, Henan province, and the scale was analyzed for item analysis, reliability testing, and structural validity assessment. From September to October 2023, another survey was conducted to carry out confirmatory factor analysis and criterion-related validity assessment. 
Results  The intravenous thrombolysis intention scale for acute ischemic stroke comprised one overall evaluation item and three dimensions, totaling 13 items. The content validity index at the scale level was 0.9. Exploratory factor analysis extracted three common factors, contributing to a cumulative variance rate of 70.515% after rotation. The Cronbach’s α coefficient of the scale was 0.904, the split-half reliability was 0.840, and the test-retest reliability was 0.872. The results of the confirmatory factor analysis indicated that the scale demonstrated robust convergent and discriminant validity. The correlation coefficient between the overall scale score and the chronic disease health literacy scale score was 0.671 (P<0.001), and the correlation coefficient with respondents’ perceived risk of intracerebral hemorrhage complications after thrombolysis was -0.402 (P<0.001). 
Conclusions  The intravenous thrombolysis intention scale for acute ischemic stroke exhibits good reliability and validity, and can be used to assess the intention level of intravenous thrombolysis in cases of acute ischemic stroke.