For patients with symptomatic non-acute intracranial artery total occlusion(NIATO),successful endovascular recanalization can improve the clinical prognosis of some patients.High-resolution magnetic resonance vessel wall imaging(HR-VWI)can qualitatively and quantitatively describe the characteristics of intracranial arterial lesions,which is helpful for preoperative evaluation,selecting suitable patients,guiding intraoperative treatment and regular follow-up.This article systematically reviewed the application progress of HR-VWI in the endovascular recanalization of NIATO,analyzed the correlation between HR-VWI characteristics and technical success rates for recanalization as well as perioperative complications,and discussed the limitations and future development directions of current research.

Intracranial atherosclerosis(ICAS)related acute large vessel occlusion(LVO)is an important cause of ischemic stroke,posing a serious threat to public health.Recent studies have shown that there are significant differences in the incidence of ICAS-LVO between Eastern and Western populations,which will directly affect the formulation and implementation of clinical diagnosis and treatment strategies.With the rapid development of neurointerventional techniques,the clinical management model of ICAS-LVO is undergoing profound changes.A series of new techniques and concepts have been emerging from early identification to endovascular treatment,bringing new hope for improving the prognosis of ICAS-LVO patients.This article discussed the identification of clinical and imaging features of ICAS-LVO,the optimization of endovascular treatment strategies,and the application of cutting-edge technologies,and look forward to the development direction of personalized,precise diagnosis and treatment in the future,aiming to provide new ideas and methods for clinical practice of ICAS-LVO.

Objective To analyze the effects of intravenous tirofiban following endovascular treatment on the prognosis of patients with acute anterior circulation large vessel occlusion stroke who did not achieve successful recanalization.Methods This retrospective study included consecutive patients with acute anterior circulation ischemic stroke who underwent endovascular treatment but did not achieve successful recanalization.These patients were retrospectively enrolled from the Department of Neurology at Nanjing Jinling Hospital,Affiliated Hospital of Medical School,Nanjing University,the Second Affiliated Hospital of Soochow University,Wuhan No.1 Hospital,and Yijishan Hospital of Wannan Medical College(the First Affiliated Hospital of Wannan Medical College)between January 2015 and April 2023.Baseline and clinical data were collected including age,sex,medical history(hypertension,diabetes,atrial fibrillation,hyperlipidemia),personal history(smoking and drinking),National Institutes of Health stroke scale(NIHSS)score at admission,trial of Org 10172 in acute stroke treatment(TOAST)classification(large artery atherosclerosis,cardioembolism,or other types),Alberta stroke program early CT score(ASPECTS)on admission,intravenous thrombolysis,onset-to-puncture time(OTP),collateral circulation status(poor collaterals:American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASTIN/SIR]score 0-2;good collateral:ASTIN/SIR score 3-4),procedure duration(from femoral or radial artery puncture to device removal),occlusion site,number of passes for thrombus retrieval,and post-procedural modified thrombolysis in cerebral infarction(mTICI)grade.Patients were divided into a tirofiban group and a non-tirofiban group based on whether they received post-procedural intravenous tirofiban.The primary outcome is a favorable functional outcome at 90-day since onset(modified Rankin scale[mRS]score ≤ 3).Safety outcomes included mortality at 90-day since onset(mRS score 6),any intracranial hemorrhage within 24 h post-procedure,and symptomatic intracranial hemorrhage within 24 h post-procedure.1∶1 propensity score matchings using the nearest neighbor method was performed based on variables with P＜0.05 in the comparison between the tirofiban and non-tirofiban groups.Differences in primary and safety outcomes between the matched groups were compared.Univariate and multivariate Logistic regression analyses were conducted with favorable outcomes at 90 days as the dependent variable to evaluate the effect of intravenous tirofiban administration after endovascular therapy on 90-day prognosis in patients without successful recanalization.Results A total of 356 patients without successful recanalization after endovascular treatment were included,comprising 195 males and 161 females,with a median age of 70(61,78)years(32-92 years).Among them,76 and 280patients were assigned to the tirofiban and non-tirofiban groups,respectively.At 90 days,114 patients had favorable outcomes,while 242 had unfavorable outcomes.(1)Before the 1∶1 propensity score matching,significant differences were observed between the tirofiban and non-tirofiban groups in terms of admission NIHSS score,ASPECTS,good collateral circulation,intravenous thrombolysis,and mTICI grade(all P＜0.05).After 1∶1 propensity score matching,66 matched pairs(132 patients)were obtained.No significant differences in the baseline and clinical characteristics were found between the two groups after matching(all P＞0.05).(2)After 1∶1 matching,a significant difference was observed in the rate of favorable outcomes at 90 days between the two groups(48.5％[32/66]vs.30.3％[20/66],P=0.033)after propensity score matching,while no significant differences were found in 90-day mortality,intracranial hemorrhage within 24h,or symptomatic intracranial hemorrhage within 24h(all P＞0.05).(3)Univariate analysis after 1∶1 matching indicated that age,atrial fibrillation,smoking history,admission NIHSS score,M2 segment middle cerebral artery occlusion,good collateral circulation,number of passes for thrombus retrieval,and tirofiban treatment were factors influencing favorable outcomes at 90 days.Multivariate Logistic regression analysis showed that younger age(aOR,0.942,95％CI 0.906-0.978,P=0.002),lower admission NIHSS score(aOR,0.855,95％CI 0.777-0.941,P=0.001),good collateral circulation(aOR,5.534,95％CI 2.141-14.301,P＜0.01),tirofiban treatment(aOR,2.774,95％CI 1.092-7.046,P=0.032),and M2 segment MCA occlusion(reference:internal carotid artery occlusion;aOR,4.874,95％CI 1.428-16.632,P=0.011)were independent predictors of favorable outcomes at 90 days.Conclusions Intravenous tirofiban administration after endovascular therapy may improve 90-day neurological outcomes in patients with acute anterior circulation large vessel occlusion stroke who did not achieve successful recanalization without increasing the risk of hemorrhage.Further large-scale randomized controlled trials are warranted to validate these findings.

Ischemic stroke,a major public health challenge,is marked by high incidence,disability,and mortality rates.The pathophysiology of stroke is complex,and inflammation reaction plays a crucial role throughout the process.In recent years,anti-inflammatory treatment has become a major focus in researches of ischemic stroke.Current preclinical and clinical investigations have revealed the potential of various anti-inflammatory strategies for ischemic stroke.However,which patient populations benefit from existing drugs are not yet elucidated.Additionally,drugs targeting innate immune cascades have become a new research hot spot.This article reviewed the latest clinical advances in anti-inflammatory therapy for ischemic stroke and discusses its promising prospects,aiming to provide novel clinical application strategies and future research directions.

Objective:To investigate whether escin(ESC)and dextromethorphan(DEX)have the protective effects on the progression and symptoms of Alzheimer disease(AD).Methods:The AD model of Caenorhabditis elegans(C.elegans)was established by transgenic amyloid β-protein(Aβ protein).Different concentrations of ESC or DEX or 50 μmol/L memantine(MEM)were used to treat the AD model worms,and their lifespan was detected.The movement ability of AD model C.elegans was evalua-ted by body bending frequency and head swinging frequency.The changes in cognitive functions of AD model C.elegans before and after treatment were detected by chemotaxis experiments.The changes in A βprotein and reactive oxygen species(ROS)content in C.elegans were detected.The changes in gene pathways related to oxidative stress were detected by Real-time quantitative polymerase chain reaction(RT-qPCR).Results:At high dose 1 000 μmol/L,ESC or DEX treatment showed no significant effects on the activity of C.elegans.Compared with untreated worms,the survival time of AD model C.elegans in the 20 μmol/L ESC and 60 μmol/L DEX intervention groups was significantly extended.In the middle stage of AD progression,the body bending frequency and head swinging frequency of AD model worms after ESC or DEX treatment was significantly increased compared with the untreated control group with DEX being more effective in the recovery of head swinging frequency.For the early cognitive function tests,the chemotaxis index of ESC or DEX treated worms was significantly higher than that of the untreated worms,which correlated with marked reductions in the Aβ protein levels.The reactive oxygen species content in the drug intervention group was also lower than that in the control group.RT-qPCR results showed that ESC could inhibit oxidative stress in the AD model C.elegans by a 2-fold upregulation of skn1 expression.Conclusion:ESC and DEX could improve the reductions of movement ability and cognitive function in the AD model worms and delay the aggravation of AD-related symptoms.ESC delays the progression of AD pos-sibly by activating the SKN-1/Nrf2 pathway to protect against oxidative injury in the AD model.

Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

Secondary white matter injury after ischemic stroke refers to the secondary changes in the white matter structure outside the infarct site that are not directly damaged after stroke, often involving key connecting areas such as the corpus callosum and contralateral internal capsule. This injury is closely associated with post-stroke cognitive impairment, motor dysfunction, and language disorders, which affects the long-term outcome. Its pathophysiological mechanisms mainly include immune and inflammatory imbalance, excitotoxicity and mitochondrial dysfunction, blood-brain barrier damage, axonal injury, and remyelination disorder. Exploring these mechanisms in depth can help promote early diagnosis and intervention of secondary white matter damage after ischemic stroke, provide theoretical basis for the development of targeted treatment strategies, and promote functional recovery in patients with stroke.

Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

For patients with symptomatic non-acute intracranial artery total occlusion(NIATO),successful endovascular recanalization can improve the clinical prognosis of some patients.High-resolution magnetic resonance vessel wall imaging(HR-VWI)can qualitatively and quantitatively describe the characteristics of intracranial arterial lesions,which is helpful for preoperative evaluation,selecting suitable patients,guiding intraoperative treatment and regular follow-up.This article systematically reviewed the application progress of HR-VWI in the endovascular recanalization of NIATO,analyzed the correlation between HR-VWI characteristics and technical success rates for recanalization as well as perioperative complications,and discussed the limitations and future development directions of current research.

Intracranial atherosclerosis(ICAS)related acute large vessel occlusion(LVO)is an important cause of ischemic stroke,posing a serious threat to public health.Recent studies have shown that there are significant differences in the incidence of ICAS-LVO between Eastern and Western populations,which will directly affect the formulation and implementation of clinical diagnosis and treatment strategies.With the rapid development of neurointerventional techniques,the clinical management model of ICAS-LVO is undergoing profound changes.A series of new techniques and concepts have been emerging from early identification to endovascular treatment,bringing new hope for improving the prognosis of ICAS-LVO patients.This article discussed the identification of clinical and imaging features of ICAS-LVO,the optimization of endovascular treatment strategies,and the application of cutting-edge technologies,and look forward to the development direction of personalized,precise diagnosis and treatment in the future,aiming to provide new ideas and methods for clinical practice of ICAS-LVO.