The methyltransferase METTL3 regulates endothelial cell proliferation and inflammation via m6A RNA methylation-mediated TRAF1 expression.

The imbalance of vascular endothelial cell homeostasis is the key mechanism for the progression of many vascular diseases. RNA modification, particularly N6-Methyladenosine (m6A), plays important function in numerous biological processes. Nevertheless, the regulatory function of m6A RNA methylation in endothelial dysfunction remains insufficiently characterized. In this study, we established that the m6A methyltransferase METTL3 is critical for regulating endothelial function. Functionally, depletion of METTL3 results in decreased endothelial cells proliferation, survival and inflammatory response. Conversely, overexpression of METTL3 elicited the opposite effects. Mechanistically, MeRIP-seq identified that METTL3 catalyzed m6A modification of TRAF1 mRNA and enhanced TRAF1 translation, thereby up-regulation of TRAF1 protein. Over-expression of TRAF1 successfully rescued the inhibition of proliferation and adhesion of endothelial cells due to METTL3 knockdown. Additionally, m6A methylation-mediated TRAF1 expression can be reversed by the demethylase ALKBH5. Knockdown of ALKBH5 upregulated the level of m6A and protein level of TRAF1, and also increased endothelial cells adhesion and inflammatory response. Collectively, our findings suggest that METTL3 regulates vascular endothelium homeostasis through TRAF1 m6A modification, suggesting that targeting the METTL3-m6A-TRAF1 axis may hold therapeutic potential for patients with vascular diseases.

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.

Objective: Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening. Methods: The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were "Gaussian Naive Bayes (GNB)", "Support Vector Machine (SVM)", "K Neighbors Classifier (KNN)", and "Logistic Regression (LR)". Finally, the optimal algorithm was selected to construct the final prediction model. Results: Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively. Conclusion: We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

Antitumor effects of different Ganoderma lucidum spore powder in cell- and zebrafish-based bioassays.

OBJECTIVE: Ganoderma lucidum spore (GLS) is gaining recognition as a medicinal part of G. lucidum and has been reported to possess various pharmacological properties, such as antitumor activity. In this work, wall-broken GLS powder (BGLSP) and wall-removed GLS powder (RGLSP), two kinds of GLS powder with different manufacturing techniques, were compared in terms of contents of active constituents and in vivo and in vitro antitumor effects. METHODS: The ultraviolet and visible spectrophotometry method was used to determine the contents of polysaccharides and total triterpenoids in BGLSP and RGLSP. Seventeen individual triterpenoids were further quantified using ultra-high-performance liquid chromatography and quantitative analysis of multi-components by single marker. The antitumor effects of BGLSP and RGLSP were evaluated using in vitro cell viability assay against human gastric carcinoma SGC-7901, lung carcinoma A549 and lymphoma Ramos and further validated by in vivo zebrafish xenograft models with transplanted SGC-7901, A549 and Ramos. RESULTS: The results showed that the contents of polysaccharides, total triterpenoids and individual triterpenoids of RGLSP were significantly higher than those of BGLSP. Although both BGLSP and RGLSP inhibited the three tumor cell lines in vitro in a dose-dependent manner, the inhibitory effects of RGLSP were much better than those of BGLSP. In the in vivo zebrafish assay, RGLSP exhibited more potent inhibitory activities against tumors transplanted into the zebrafish compared with BGLSP, and the inhibition rates of RGLSP reached approximately 78%, 31% and 83% on SGC-7901, A549 and Ramos, respectively. CONCLUSION: The results indicated that the antitumor effects of GLS were positively correlated with the contents of the polysaccharides and triterpenoids and demonstrated that the wall-removing manufacturing technique could significantly improve the levels of active constituents, and thereby enhance the antitumor activity.