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Background: The study aimed to investigate the association between nonalcoholic fatty liver disease (NAFLD) and ischemic stroke events after revascularization in patients with Moyamoya disease (MMD). Methods: This study prospectively enrolled 275 MMD patients from September 2020 to December 2021. Patients with alcoholism and other liver diseases were excluded. NAFLD was confirmed by CT imaging or abdominal ultrasonography. Stroke events and modified Rankin Scale (mRS) scores at the latest follow-up were compared between the two groups. Results: A total of 275 patients were enrolled in the study, among which 65 were diagnosed with NAFLD. Univariate logistic regression analysis showed that NAFLD (P = 0.029) was related to stroke events. Multivariate logistic regression analysis showed that NAFLD is a predictor of postoperative stroke in MMD patients (OR = 27.145, 95% CI = 2.031–362.81, P = 0.013). Kaplan-Meier analysis showed that compared with MMD patients with NAFLD, patients in the control group had a longer stroke-free time (P = 0.004). Univariate Cox analysis showed that NAFLD (P = 0.016) was associated with ischemic stroke during follow-up in patients with MMD. Multivariate Cox analysis showed that NAFLD was an independent risk factor for stroke in patients with MMD (HR = 10.815, 95% CI = 1.259–92.881, P = 0.030). Furthermore, fewer patients in the NAFLD group had good neurologic status (mRS score ≤ 2) than the control group (P = 0.005). Conclusion: NAFLD was an independent risk factor for stroke in patients with MMD after revascularization and worse neurological function outcomes. [ABSTRACT FROM AUTHOR]

Background: Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. Methods: A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical‐radiological semantic features; (2) The radiomics models (radiomics+ and radiomics−) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite−) incorporated both above. Results: In the testing set, the radiomics+/− models and the composite+/− models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/− models and the composite+/− models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/− and composite+/− models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics−, composite+ model vs. composite−) (p > 0.05). Conclusions: The present study established a machine learning‐based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability. [ABSTRACT FROM AUTHOR]

Background: Patients with nonischemic dilated cardiomyopathy (NIDCM) are prone to arrhythmias, and the cause of mortality in these patients is either end-organ dysfunction due to pump failure or malignant arrhythmia-related death. However, the identification of patients with NIDCM at risk of malignant ventricular arrhythmias (VAs) is challenging in clinical practice. The aim of this study was to evaluate whether cardiovascular magnetic resonance feature tracking (CMR-FT) could help in the identification of patients with NIDCM at risk of malignant VAs. Methods: A total of 263 NIDCM patients who underwent CMR, 24-hour Holter electrocardiography (ECG) and inpatient ECG were retrospectively evaluated. The patients with NIDCM were allocated to two subgroups: NIDCM with VAs and NIDCM without VAs. From CMR-FT, the global peak radial strain (GPRS), global longitudinal strain (GPLS), and global peak circumferential strain (GPCS) were calculated from the left ventricle (LV) model. We investigated the possible predictors of NIDCM combined with VAs by univariate and multivariate logistic regression analyses. Results: The percent LGE (15.51 ± 3.30 vs. 9.62 ± 2.18, P < 0.001) was higher in NIDCM patients with VAs than in NIDCM patients without VAs. Furthermore, the NIDCM patients complicated with VAs had significantly lower GPCS than the NIDCM patients without VAs (− 5.38 (− 7.50, − 4.22) vs.−9.22 (− 10.73, − 8.19), P < 0.01). Subgroup analysis based on LGE negativity showed that NIDCM patients complicated with VAs had significantly lower GPRS, GPCS, and GPLS than NIDCM patients without VAs (P < 0.05 for all). Multivariate analysis showed that both GPCS and %LGE were independent predictors of NIDCM combined with VAs. Conclusions: CMR global strain can be used to identify NIDCM patients complicated with VAs early, specifically when LGE is not present. GPCS < − 13.19% and %LGE > 10.37% are independent predictors of NIDCM combined with VAs. [ABSTRACT FROM AUTHOR]

Mitochondrial DNA (mtDNA) copy number (CN) is a biomarker of mitochondrial function and has been reported associated with kidney disease. However, its association with IgA nephropathy (IgAN), the most common cause of glomerulonephritis (GN), has not been evaluated. We included 664 patients with biopsy-proven IgAN and measured mtDNA-CN in peripheral blood by multiplexed real-time quantitative polymerase chain reaction (RT-qPCR). We examined the associations between mtDNA-CN and clinical variables and found that patients with higher mtDNA-CN had higher estimated glomerular filtration rate (eGFR) (r = 0.1009, p =.0092) and lower serum creatinine (SCr), blood urea nitrogen (BUN), and uric acid (UA) (r=−0.1101, −0.1023, −0.07806, respectively, all p values <.05). In terms of pathological injury, mtDNA-CN was higher in patients with less mesangial hypercellularity (p =.0385, M0 vs. M1 score by Oxford classification). Multivariable logistic regression analyses also showed that mtDNA-CN was lower for patients with moderate to severe renal impairment (defined as eGFR < 60 mL/min/1.73 m2) vs. mild renal impairment, with the odds ratio of 0.757 (95% confidence interval: 0.579–0.990, p =.042). In conclusion, mtDNA-CN was correlated with better renal function and less pathological injury in patients with IgAN, proposing that systemic mitochondrial dysfunction may be involved in or reflect the development of IgAN. [ABSTRACT FROM AUTHOR]

Background: Few studies have identified the links between physical activity (PA), clinical symptoms, and the quality of life (QoL) among mildly infected individuals with COVID-19. This cross-sectional study aims to evaluate how PA levels before infections affect the infectious symptoms and the QoL in mildly infected patients with COVID-19. Methods: An online questionnaire link including participants' sociodemographic and anthropometric characteristics, clinical symptoms during the COVID-19 infectious period, the QoL of the worst symptomatic day, and PA in the last seven days before COVID-19 infections was disclosed. Logistic regression and multiple linear regression analyses were applied to assess the relationships between PA levels in the last seven days before infections and COVID-19-related outcomes. The level of statistical significance was set at p < 0.05. Results: Compared to the low-PA-level group, the moderate-PA-level group presented a higher risk of headaches (OR = 1.34, 95% CI = 1.03 to 1.75, and p = 0.03) and the high-PA-level group presented a higher risk of muscle/body aches (OR = 1.42, 95% CI = 1.04 to 1.93, and p = 0.03). The adjusted linear regression analysis showed that no associations were found between PA levels in the last seven days before infections and the QoL index value on the worst symptomatic day (moderate-PA-level group: β = −0.04, and p = 0.08; high-PA-level group: β = −0.04, and p = 0.17). However, for the mobility and usual activities dimensions of EQ-5D-5L, the lower-PA-level group had a lower burden of QoL than the higher-PA-level group did on the worst-symptomatic day. Conclusions: Among mildly infected patients with COVID-19, a higher PA level is associated with a higher risk of experiencing clinical symptoms and a lower QoL. [ABSTRACT FROM AUTHOR]

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]

Background: To investigate the risk factors for early seizure after revascularization in patients with moyamoya disease (MMD). Methods: A total of 298 patients with MMD diagnosed in our hospital from 2015 to 2018 were analyzed retrospectively. We summarized the characteristics of seizure after revascularization in patients with MMD and analyzed the predictors of early postoperative seizure. Results: We identified 15 patients with MMD who developed seizures within 1 week after revascularization. According to logistic regression analysis, age (OR: 1.04, 95% CI 0.998–1.086; P = 0.060) and infarct side (OR: 1.92, 95% CI 0.856–4.290; P = 0.113) were not significantly associated with incident early seizure. Postoperative infarction (OR: 12.89, 95% CI 4.198–39.525; P = 0.000) and preoperative cerebral infarction (OR: 4.08, 95% CI 1.267–13.119; P = 0.018) were confirmed as risk factors for early seizure. Conclusions: We believe that a history of preoperative infarction and new infarction are independent risk factors of early seizure in patients with MMD after revascularization. [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]

Objective: To investigate the angioarchitectural factors associated with postoperative cerebral infarction in ischemic moyamoya disease. Methods: Data on patients who underwent surgery for ischemic MMD from 1 October 2015 to 31 October 2020, at Peking University International Hospital were collected and retrospectively analyzed. General conditions such as patient sex, age, site of surgery, preoperative manifestations such as TIA attack and old cerebral infarction, and seven angioarchitectural factors of the MMD based on DSA were selected and measured. Statistical analysis was performed by the Pearson chi-square statistic, analysis of variance (ANOVA), and multifactor logistic regression analysis. Results: Age (OR, 0.969; 95%CI, 0.939–1.000; p = 0.049), A1stenosis (OR, 5.843; 95%CI, 1.730–19.732; p = 0.004), M1stenosis (OR, 6.206; 95%CI, 2.079–18.526; p = 0.001), PCA anomalies (OR, 4.367; 95%CI, 1.452–13.129; p = 0.049), Unstable compensation (OR, 5.335; 95%CI, 1.427–19.948; p = 0.013), TIA (OR, 4.264; 95%CI, 1.844–9.863; p = 0.001), Old cerebral infarction (OR, 2.972; 95%CI, 1.194–7.397; p = 0.019). The above seven factors can be used in the regression equation to predict the probability of postoperative cerebral infarction. The prediction accuracy is 90.2%. Conclusions: Age, TIA attack, old cerebral infarction, and five angioarchitectural factors of MMD are strongly associated with postoperative cerebral infarction. Seven factors, including age, TIA attack, old infarction, and four angioarchitectural factors, can be taken to quantify the probability of surgical cerebral infarction in MMD. [ABSTRACT FROM AUTHOR]