Background: Hyperintense vessels (HVs) on fluid-attenuated inversion recovery (FLAIR) are frequently observed in acute ischemic stroke (AIS). The presence of HVs represents altered blood flow from collaterals distal to arterial occlusion or stenosis. This study aimed to evaluate the prognostic value of HVs in AIS. Methods: Fifty-four consecutive patients with acute middle cerebral artery occlusion were enrolled in the study. The location and extent of the HVs was determined by FLAIR. Clinical data were obtained and compared between patients with different grades of HVs. Additionally, the relationship between distal HVs and leptomeningeal collaterals (LMCs) was assessed using angiography. Results: HVs were observed in 41 (75.9%) of the 54 patients enrolled. The initial NIHSS score was lower (p < 0.001) and the infarction volume was smaller (p < 0.001) in patients with distal HVs. Adjusting of other factors, regression analysis revealed that distal HVs are an independent predictor of a favorable outcome at 90 days (p = 0.006; OR 0.049; 95% CI 0.006–0.420). Furthermore, the presence of distal HVs was correlated with the presence of LMCs. Conclusion: Distal HVs may be a marker for LMCs and act as a predictor of a favorable clinical outcome for patients with AIS.

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This study indicated that increased levels of circulating SDF-1α and CD34(+) CXCR4(+) cells in patients with MMD, which may play an important role in the vasculogenesis in MMD.

PurposeThe aim of this study was to compare the serum levels of one-carbon metabolism (OCM) nutrients (e.g., folate, homocysteine and vitamin B12) and peripheral blood DNA methylation in epileptic patients under treatment with antiepileptic drugs (AEDs) and in healthy controls.MethodsIn this cross-sectional study, 60 patients with epilepsy who were receiving valproate (VPA) (n = 30) or lamotrigine (LTG) (n = 30) monotherapy were enrolled. Thirty age and sex matched healthy subjects served as the controls. Serum concentrations of OCM nutrients and peripheral blood DNA methylation status were measured.ResultsCompared to the control group, the VPA group had higher serum levels of homocysteine (p<0.05). No difference in homocysteine concentration was observed in the LTG group. Patients receiving VPA or LTG had significantly lower serum folate levels in comparison with controls (p<0.001). The level of methylation of long interspersed nucleotide element-1 (LINE-1) in peripheral blood was not significantly different between the AED monotherapy group and healthy controls. A difference in the methylation levels of methylenetetrahydrofolate reductase (MTHFR) amplicon was observed between AED-treated patients with epilepsy and controls (p<0.01). A positive correlation between serum folate levels and peripheral blood MTHFR amplicon methylation status was also observed (r = 0.25, p = 0.023).ConclusionOur findings suggest that the effects of AED monotherapy on OCM may induce specific regions of DNA hypomethylation.

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ImportanceSelection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the “right drugs” are prescribed.ObjectiveTo develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.Design, Setting, and ParticipantsThis cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables.ExposuresOne of 7 antiseizure medications.Main Outcomes and MeasuresWith the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models.ResultsThe final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC.Conclusions and RelevanceIn this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.