Efficacy and Safety of Ginkgo Diterpene Lactone Meglumine in Acute Ischemic Stroke: A Randomized Clinical Trial.

Importance: Ginkgo diterpene lactone meglumine (GDLM) has attracted much attention because of its potential neuroprotective properties in ischemic stroke. The efficacy of GDLM in patients with acute ischemic stroke (AIS) needs to be verified by well-designed randomized clinical trials. Objective: To assess the efficacy and safety of GDLM in patients with AIS. Design, Setting, and Participants: This multicenter, randomized, double-blind, placebo-controlled, parallel-group trial involved 3448 patients who had AIS, were aged 18 to 80 years, had a clinically diagnosed AIS symptom within 48 hours of onset, had a modified Rankin Scale (mRS) score of 0 or 1 prior to onset, and had a National Institutes of Health Stroke Scale score ranging from 4 to 24. The trial took place at 100 centers in China from February 1, 2016, to May 1, 2018. The mRS is a global stroke disability scale with scores ranging from 0 (no symptoms or completely recovered) to 6 (death). The National Institutes of Health Stroke Scale is a tool used by clinicians to quantify impairment caused by stroke (range, 0-42, with higher scores indicating greater severity). Data were analyzed from January 2019 to December 2022. Interventions: Patients were randomized to receive GDLM or placebo once daily via intravenous infusion in a 1:1 ratio. The treatment was dispensed within 48 hours after symptoms and continued for 14 days. Interventions of thrombolysis and thrombectomy were not permitted during the treatment. Main Outcomes and Measures: The primary outcome was the proportion of patients with an mRS of 0 or 1 on day 90 after randomization. Safety outcomes included adverse events and serious adverse events. Results: A total of 3448 patients were randomized, with 1725 patients assigned to the GDLM group and 1723 patients assigned to the placebo group. The median (IQR) age of the patients was 63 (55-71) years, and 1232 (35.7%) were women. The primary outcome on day 90 occurred in 877 patients (50.8%) in the GDLM group, and 759 patients (44.1%) in the placebo group (risk difference, 6.79%; 95% CI, 3.46%-10.10%; odds ratio, 1.31; 95% CI, 1.15-1.50; relative risk, 1.15; 95% CI, 1.08-1.24; P < .001). Adverse events occurred relatively equally between the 2 groups (303 [17.6%] vs 298 [17.3%]; risk difference, 0.27%; 95% CI, -2.26% to 2.80%; odds ratio, 1.02; 95% CI, 0.85-1.21; relative risk, 1.02; 95% CI, 0.88-1.17; P = .83). Conclusions and Relevance: Among patients with AIS in this randomized clinical trial, GDLM improved the proportion of patients achieving favorable clinical outcomes at 90 days compared with placebo. Trial Registration: ClinicalTrials.gov Identifier: NCT02526225.

Synthetic Epileptic Brain Activities with TripleGAN.

Epilepsy is a chronic noninfectious disease caused by sudden abnormal discharge of brain neurons, which leads to intermittent brain dysfunction. It is also one of the most common neurological diseases in the world. The automatic detection of epilepsy based on electroencephalogram through machine learning, correlation analysis, and temporal-frequency analysis plays an important role in epilepsy early warning and automatic recognition. In this study, we propose a method to realize EEG epilepsy recognition by means of triple genetic antagonism network (GAN). TripleGAN is used for EEG temporal domain, frequency domain, and temporal-frequency domain, respectively. The experiment was conducted through CHB-MIT datasets, which operated at the latest level in the same industry in the world. In the CHB-MIT dataset, the classification accuracy, sensitivity, and specificity exceeded 1.19%, 1.36%, and 0.27%, respectively. The crossobject ratio exceeded 0.53%, 2.2%, and 0.37%, respectively. It shows that the established deep learning model of TripleGAN has a good effect on EEG epilepsy classification through simulation and classification optimization of real signals.

Rheumatoid Arthritis and Cardio-Cerebrovascular Disease: A Mendelian Randomization Study.

Significant genetic association exists between rheumatoid arthritis (RA) and cardiovascular disease. The associated mechanisms include common inflammatory mediators, changes in lipoprotein composition and function, immune responses, etc. However, the causality of RA and vascular/heart problems remains unknown. Herein, we performed Mendelian randomization (MR) analysis using a large-scale RA genome-wide association study (GWAS) dataset (462,933 cases and 457,732 controls) and six cardio-cerebrovascular disease GWAS datasets, including age angina (461,880 cases and 447,052 controls), hypertension (461,880 cases and 337,653 controls), age heart attack (10,693 cases and 451,187 controls), abnormalities of heartbeat (461,880 cases and 361,194 controls), stroke (7,055 cases and 454,825 controls), and coronary heart disease (361,194 cases and 351,037 controls) from United Kingdom biobank. We further carried out heterogeneity and sensitivity analyses. We confirmed the causality of RA with age angina (OR = 1.17, 95% CI: 1.04-1.33, p = 1.07E-02), hypertension (OR = 1.45, 95% CI: 1.20-1.75, p = 9.64E-05), age heart attack (OR = 1.15, 95% CI: 1.05-1.26, p = 3.56E-03), abnormalities of heartbeat (OR = 1.07, 95% CI: 1.01-1.12, p = 1.49E-02), stroke (OR = 1.06, 95% CI: 1.01-1.12, p = 2.79E-02), and coronary heart disease (OR = 1.19, 95% CI: 1.01-1.39, p = 3.33E-02), contributing to the understanding of the overlapping genetic mechanisms and therapeutic approaches between RA and cardiovascular disease.

Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis.

With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning capacity has been applied in diagnosing different types of diseases. This study reviews the application of expert systems, neural networks, and deep learning used by AI technology in disease diagnosis. This paper also gives a glimpse of the intelligent diagnosis and treatment of digestive system diseases, respiratory system diseases, and osteoporosis by AI technology.

AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine.

Antioxidant proteins play important roles in countering oxidative damage in organisms. Because it is time-consuming and has a high cost, the accurate identification of antioxidant proteins using biological experiments is a challenging task. For these reasons, we proposed a model using machine-learning algorithms that we named AOPs-SVM, which was developed based on sequence features and a support vector machine. Using a testing dataset, we conducted a jackknife cross-validation test with the proposed AOPs-SVM classifier and obtained 0.68 in sensitivity, 0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC, and 0.832 in AUC. This outperformed existing classifiers. The experiment results demonstrate that the AOPs-SVM is an effective classifier and contributes to the research related to antioxidant proteins. A web server was built at http://server.malab.cn/AOPs-SVM/index.jsp to provide open access.

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features.

To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.