I n cerebral ischemia due to large-vessel occlusion, cell viability depends on the collateral perfusion status (1,2). Infarction might be complete in less than 1 hour or may not be complete for hours or days depending on the collateral perfusion status, which varies among patients (3,4). Therefore, recanalization and reperfusion treatment can be futile or even dangerous when performed in the standard optimal time window, but could be useful if performed in a later time window (5)(6)(7)(8). Studies have shown that a large ischemic core combined with poor collaterals is a strong predictor of an unfavorable response to endovascular treatment and poor functional outcomes. Hence, the exclusion of patients with a large ischemic core and poor collateral circulation could prevent administration of futile and dangerous recanalization therapy. Conversely, good collateral circulation can limit ischemic core expansion and prolong the time the penumbral tissue at risk remains salvageable until reperfusion therapy. Some studies have suggested that the time window for endovascular treatment can be successfully extended in patients with good collaterals (9-14). These results demonstrate the importance of accurately estimating the collateral perfusion status to enable patientspecific application of treatment, thereby improving functional outcomes among patients with acute ischemic stroke due to large-vessel occlusion.Recently, researchers have developed dynamic collateral imaging methods such as multiphase CT angiography and collateral flow maps derived from dynamic susceptibility contrast material-enhanced perfusion MRI (15,16). Studies evaluating these collateral imaging approaches have shown that collateral status is a strong predictor of final infarct size and functional outcomes in patients considered eligible for intra-arterial thrombectomy and intravenous thrombolysis (17,18). For intra-arterial thrombectomy, it

We investigated the role of near infrared spectroscopy (NIRS) in identifying delayed cerebral ischemia (DCI) in patients with subarachnoid hemorrhage (SAH). We measured the cerebral regional oxygen saturation (rSO2) continuously for 14 days. The differences in rSO2 according to DCI were analyzed. We also compared the diagnostic accuracy of NIRS and transcranial Doppler ultrasonography (TCD) for DCI detection using the area under receiver operator characteristic (ROC) curve. Fifty-two patients treated with coil embolization were enrolled, including 18 with DCI (34.6%) and 34 without DCI (65.4%). Significant differences in rSO2 levels were observed from days 7 to 9. The rSO2 level was 60.95 (58.10–62.30) at day 7 in the DCI vs. 63.90 (62.50–67.10) in the non-DCI patients. By day 8, it was 59.50 (56.90–64.50) in the DCI vs. 63.30 (59.70–68.70) in the non-DCI cases. By day 9, it was 61.85 (59.40–65.20) in the DCI vs. 66.00 (62.70–68.30) in the non-DCI. A decline of >12.7% in SO2 rate yielded a sensitivity of 94.44% (95% CI: 72.7–99.9%) and a specificity of 70.59% (95% CI: 52.5–84.9%) for identifying DCI. Changes in NIRS tended to yield better diagnostic accuracy than TCD, but were not statistically significant. NIRS is a feasible method for real-time detection of DCI.

Objective Conflicting results regarding SOX17 genes and the risk of intracranial aneurysms (IA) exist in the Korean population, although significant positive correlations were noted in genome-wide association studies in European and Japanese populations. Therefore, we aimed to investigate an association between SOX17 gene variants and IA using exome sequencing data. Methods This study included 26 age-gender matched IA patients and 26 control subjects. The SOX17 gene variants identified from whole-exome sequencing data were examined. Genetic associations to estimate odds ratio (OR) and 95% confidence interval (CI) were performed using the software EPACTS. Results The mean age of the IA and control groups were 51.0±9.3 years and 49.4±14.3 years, respectively ( p =0.623). Seven variants of SOX17 , including six single nucleotide polymorphisms and one insertion and deletion, were observed. Among these variants, rs12544958 (A>G) showed the most association with IA, but the association was not statistically significant (OR, 1.97; 95% CI, 0.81–4.74; p =0.125). Minor allele frequencies of the IA patients and controls were 0.788 and 0.653, respectively. None of the remaining variants were significantly associated with IA formation. Conclusion No significant association between SOX17 gene variants and IA were noted in the Korean population. A large-scale exome sequencing study is necessary to investigate any Korean-specific genetic susceptibility to IA.

No abstract

Polygenic risk scores (PRSs) have an important relevance to approaches for clinical usage in intracranial aneurysm (IA) patients. Hence, we aimed to develop IA-predicting PRS models including the genetic basis shared with acute ischemic stroke (AIS) in Korean populations. We applied a weighted PRS (wPRS) model based on a previous genome-wide association study (GWAS) of 250 IA patients in a hospital-based multicenter cohort, 222 AIS patients in a validation study, and 296 shared controls. Risk predictability was analyzed by the area under the receiver operating characteristic curve (AUROC). The best-fitting risk models based on wPRSs were stratified into tertiles representing the lowest, middle, and highest risk groups. The weighted PRS, which included 29 GWASs (p < 5×10−8) and two reported genetic variants (p < 0.01), showed a high predictability in IA patients (AUROC = 0.949, 95% CI: 0.933–0.966). This wPRS was significantly validated in AIS patients (AUROC = 0.842, 95% CI: 0.808–0.876; p < 0.001). Two-stage risk models stratified into tertiles showed an increased risk for IA (OR = 691.25, 95% CI: 241.77–1976.35; p = 3.1×10−34; sensitivity/specificity = 0.728/0.963), which was replicated in AIS development (OR = 39.76, 95% CI: 16.91–93.49; p = 3.1×10−17; sensitivity/specificity = 0.284/0.963). A higher wPRS for IA may be associated with an increased risk of AIS in the Korean population. These findings suggest that IA and AIS may have a shared genetic architecture and should be studied further to generate a precision medicine model for use in personalized diagnosis and treatment.