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Moyamoya disease (MMD) is a chronic and progressive cerebrovascular stenosis or occlusive disease that occurs near Willis blood vessels. The aim of this study was to investigate the mutation of DIAPH1 in Asian population, and to compare the angiographic features of MMD patients with and without the mutation of the DIAPH1 gene. Blood samples of 50 patients with MMD were collected, and DIAPH1 gene mutation was detected. The angiographic involvement of the posterior cerebral artery was compared between the mutant group and the non-mutant group. The independent risk factors of posterior cerebral artery involvement were determined by multivariate logistic regression analysis. DIAPH1 gene mutation was detected in 9 (18%) of 50 patients, including 7 synonymous mutations and 2 missense mutations. However, the incidence of posterior cerebral artery involvement in mutation positive group was very higher than that in mutation negative group (77.8% versus 12%; p = 0.001). There is an association between DIAPH1 mutation and PCA involvement (odds ratio 29.483, 95% confidence interval 3.920–221.736; p = 0.001). DIAPH1 gene mutation is not a major genetic risk gene for Asian patients with moyamoya disease but may play an important role in the involvement of posterior cerebral artery. [ABSTRACT FROM AUTHOR]

Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques to assess hemorrhagic risk in patients with CAD. A total of 1638 patients with CAD were enrolled from January 2018 to October 2019, among which 114 patients were observed to have at least one positive event. Importantly, 102 patients with 9933 records were finally retained for analysis in this study. Participants were required to collect data using the portable PPG acquisition device and the specially designed Android APP. The data was collected and uploaded to a remote server. Based on collected PPG signals, we extracted features in a total of 30 dimensions from time-domain, frequency-domain, and wavelet packet decomposition. Logistic regression, support vector regression, random forest, and XGBoost regression models were established to achieve hemorrhagic risk evaluation, and then, their performances were compared. In total, 10 features extracted from PPG showed statistical significance (p < 0.01) between negative and positive groups. The newly established XGBoost model performed best in the hemorrhagic risk evaluation experiment, wherein the mean area under the curve (AUC) with tenfold cross-validation was 0.762 ± 0.024 and the sensitivity and specificity were 0.679 ± 0.051 and 0.714 ± 0.014, respectively. We established a data acquisition system for PPG signal collection, and demonstrated that a set of features extracted from PPG and the proposed machine learning model are promising in the evaluation of hemorrhagic risk among patients with CAD. In comparison with the traditional HAS-BLED score, the proposed method can obtain the quantitative risk prediction probability from a single PPG record, which has the advantages of dynamics and continuity, and can provide timely feedback for doctors' antithrombotic treatment, which is of great significance for doctors to quickly determine the effectiveness of the treatment and adjust the timely treatment plans accordingly. [ABSTRACT FROM AUTHOR]