ObjectiveTo investigate the effect of nicotinamide adenine dinucleotide on vascular endothelial injury in hypertension and its molecular mechanism. MethodsC57BL/6J mice were randomly divided into saline group （Saline） and hypertension group （Ang Ⅱ， which were infused with Ang Ⅱ via subcutaneously implanted osmotic pumps）， and supplemented daily with nicotinamide mononucleotide （300 mg/kg）， a precursor of NAD⁺. Blood pressure， endothelial relaxation function and pulse wave velocity were measured after 4 weeks. Wound healing assay and adhesion assay were used to evaluate the function of endothelial cells in vitro. mtROS levels were detected by immunofluorescence staining. RT-PCR was used to detect the mRNA expression of mtDNA， SIRT3 and isocitrate dehydrogenase 2 （IDH2）. 8-hydroxy-2'-deoxyguanosine levels were detected by enzyme-linked immunosorbent assay. The protein expression levels of p-eNOS， eNOS， SIRT3 and IDH2 were detected by Western blot. ResultsNMN supplementation reduced blood pressure （P<0.001） and improved endothelial function and arterial stiffness （P<0.001） in hypertensive mice. In vitro， NMN improved endothelial function in AngII-stimulated endothelial cells （P<0.05） and attenuated mitochondrial oxidative stress levels （P<0.001）. Mechanistically， NMN elevated SIRT3 activity （P<0.001）， which subsequently enhanced IDH activity （P<0.001） and reduced oxidative stress levels in endothelial cells. Conversely， knockdown of IDH2 would reverse the effect of SIRT3 in improving endothelial function （P<0.001）. ConclusionNAD⁺ lowers blood pressure and enhances vascular function in hypertension by reducing the level of oxidative stress in endothelial cells through activation of the SIRT3/IDH2 signal pathway.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: To investigate the willingness to accept preventive treatments and its related factors among college freshmen with latent tuberculosis infection (LTBI), so as to provide the evidence for preventive treatment intervention measures for students with LTBI. Methods: Cluster sampling method was used to select 368 LTBI freshmen from 8 colleges and universities in Changzhou in September 2023, who conducted a questionnaire survey on the willingness to receive preventive treatment. General demographic data were collected and relevant data were collected using tuberculosis knowledge scale, General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Adaptation, Partnership, Growth, Affection and Resolve (APGAR), and a self developed Stigma Scale. A binary Logistic regression model was constructed with the willingness to accept preventive treatment as the dependent variable to analyze the willingness to accept preventive treatment and the influencing factors. Results: A total of 253 LTBI college freshmen were willing to take preventive treatment, the acceptance rate was 68.75%. The rate of willingness to accept preventive treatment for LTBI was higher among students whose fathers had an education level of high school, compared to those whose fathers had an education level of junior high school or below ( OR =2.16, P <0.05). LTBI students whose per capita family income was >5 000-10 000 yuan and >10 000 yuan were more willing to accept LTBI preventive treatment than those whose per capita family income was <3 000 yuan ( OR =2.72, 4.46, P <0.05). LTBI students who engaged in physical exercise for more than 2 hours per week were more willing to accept than those who exercised less than 0.5 hours per week ( OR =1.91, P <0.05). LTBI students with high levels of tuberculosis knowledge and stigma were more likely to receive preventive treatment ( OR =1.18, 1.11, P < 0.05). LTBI students with high PHQ-9 ( OR =0.85) and GAD-7 ( OR =0.92) scores were more likely to refuse preventive treatment ( P <0.05). Conclusion The present study revealed a moderate level of willingness of LTBI students to preventive treatment in Changzhou City, and the acceptance is affected by family factors, healthy lifestyles, tuberculosis knowledge and psychological status.

Objective:To observe any effect of warm acupuncture on chondrocyte apoptosis and the miR-27a-mediated PI3K/AKT/mTOR signaling pathway using a rat model of early knee osteoarthritis (KOA).Methods:Fifty Sprague-Dawley rats were randomly divided into a control group, a model group, a warm acupuncture group, an inhibitor group, and an inhibitor + warm acupuncture group (the combined group), each of 10. Three days before the modeling, both the inhibitor and combined groups were injected with miR-27a inhibitor. Papain was then injected in all groups except the control group to establish the early KOA model. After successful modeling, the combined and warm acupuncture groups were given 30 minutes of warm acupuncture at the medial and lateral Xiyan points daily for 14 days. The model and inhibitor groups were fixed for 30 minutes during those sessions. After the 2 weeks, hematoxylin-eosin staining was used to observe any pathological changes in the cartilage tissue. Terminal deoxynucleoitidyl transferase-mediated nick end labeling was used to detect chondrocyte apoptosis, and enzyme-linked immunosorbent assays were employed to observe the levels of interleukin 1β (IL-1β) and IL-6. Western blotting was used to evaluate the expression of p-PI3K, p-AKT, p-mTOR, PI3K, AKT, mTOR, LC3-II, and Beclin1 proteins in the cartilage tissue, while quantitative real-time polymerase chain reactions detected the content of miR-27a.Results:After the intervention, the morphology of the chondrocytes in the warm acupuncture group had improved significantly compared to the model group, while that of the inhibitor and combined groups was better than among the warm acupuncture group. The rate of chondrocyte apoptosis in the warm acupuncture group was significantly lower than in the model group, while the rates of the inhibitor and combined groups were lower still. There was no significant difference between the inhibitor and the combination group on average. The average expression of IL-6, IL-1β, LC3-II and Beclin1 protein and of miR-27a were significantly lower in the warm acupuncture, inhibitor and combined groups than among the model group, with those of the inhibitor and combined groups significantly lower than among the warm acupuncture group, on average. The average p-PI3K/PI3K, p-AKT/AKT and p-mTOR/mTOR levels of the warm acupuncture, inhibitor and combined groups were significantly higher than those of the model group, with those of the inhibitor and combined groups significantly higher, on average, than among the warm acupuncture group. However, there was no significant difference between the inhibitor group and the combined group in their protein expression and mRNA levels.Conclusions:Warm acupuncture may down-regulate the expression of miR-27a to promote the activation of the PI3K/AKT/mTOR signaling pathway, inhibiting excessive autophagy and apoptosis. That would relieve inflammation and damage, and delay degeneration in early KOA, at least in rats.

Objective:To investigate the current status of endoscopic treatment for gastroesophageal varices in portal hypertension in China, and to provide supporting data and reference for the development of endoscopic treatment.Methods:In this study, initiated by the Liver Health Consortium in China (CHESS), a questionnaire was designed and distributed online to investigate the basic condition of endoscopic treatment for gastroesophageal varices in portal hypertension in 2022 in China. Questions included annual number and indication of endoscopic procedures, adherence to guideline for preventing esophagogastric variceal bleeding (EGVB), management and timing of emergent EGVB, management of gastric and isolated varices, and improvement of endoscopic treatment. Proportions of hospitals concerning therapeutic choices to all participant hospitals were calculated. Guideline adherence between secondary and tertiary hospitals were compared by using Chi-square test.Results:A total of 836 hospitals from 31 provinces (anotomous regions and municipalities) participated in the survey. According to the survey, the control of acute EGVB (49.3%, 412/836) and the prevention of recurrent bleeding (38.3%, 320/836) were major indications of endoscopic treatment. For primary [non-selective β-blocker (NSBB) or endoscopic therapies] and secondary prophylaxis (NSBB and endoscopic therapies) of EGVB, adherence to domestic guideline was 72.5% (606/836) and 39.2% (328/836), respectively. There were significant differences in the adherence between secondary and tertiary hospitals in primary prophylaxis of EGVB [71.0% (495/697) VS 79.9% (111/139), χ2=4.11, P=0.033] and secondary prophylaxis of EGVB [41.6% (290/697) VS 27.3% (38/139), χ2=9.31, P=0.002]. A total of 78.2% (654/836) hospitals preferred endoscopic therapies treating acute EGVB, and endoscopic therapy was more likely to be the first choice for treating acute EGVB in tertiary hospitals (82.6%, 576/697) than secondary hospitals [56.1% (78/139), χ2=46.33, P<0.001]. The optimal timing was usually within 12 hours (48.5%, 317/654) and 12-24 hours (36.9%, 241/654) after the bleeding. Regarding the management of gastroesophageal varices type 2 and isolated gastric varices type 1, most hospitals used cyanoacrylate injection in combination with sclerotherapy [48.2% (403/836) and 29.9% (250/836), respectively], but substantial proportions of hospitals preferred clip-assisted therapies [12.4% (104/836) and 26.4% (221/836), respectively]. Improving the skills of endoscopic doctors (84.2%, 704/836), and enhancing the precision of pre-procedure evaluation and quality of multidisciplinary team (78.9%, 660/836) were considered urgent needs in the development of endoscopic treatment. Conclusion:A variety of endoscopic treatments for gastroesophageal varices in portal hypertension are implemented nationwide. Participant hospitals are active to perform emergent endoscopy for acute EGVB, but are inadequate in following recommendations regarding primary and secondary prophylaxis of EGVB. Moreover, the selection of endoscopic procedures for gastric varices differs greatly among hospitals.

Background::Carotid intima-media thickness (IMT) and diameter, stiffness, and wave reflections, are independent and important clinical biomarkers and risk predictors for cardiovascular diseases. The purpose of the present study was to establish nationwide reference values of carotid properties for healthy Chinese adults and to explore potential clinical determinants.Methods::A total of 3053 healthy Han Chinese adults (1922 women) aged 18-79 years were enrolled at 28 collaborating tertiary centers throughout China between April 2021 and July 2022. The real-time tracking of common carotid artery walls was achieved by the radio frequency (RF) ultrasound system. The IMT, diameter, compliance coefficient, β stiffness, local pulse wave velocity (PWV), local systolic blood pressure, augmented pressure (AP), and augmentation index (AIx) were then automatically measured and reported. Data were stratified by age groups and sex. The relationships between age and carotid property parameters were analyzed by Jonckheere-Terpstra test and simple linear regressions. The major clinical determinants of carotid properties were identified by Pearson’s correlation, multiple linear regression, and analyses of covariance.Results::All the parameters of carotid properties demonstrated significantly age-related trajectories. Women showed thinner IMT, smaller carotid diameter, larger AP, and AIx than men. The β stiffness and PWV were significantly higher in men than women before forties, but the differences reversed after that. The increase rate of carotid IMT (5.5 μm/year in women and 5.8 μm/year in men) and diameter (0.03 mm/year in both men and women) were similar between men and women. For the stiffness and wave reflections, women showed significantly larger age-related variations than men as demonstrated by steeper regression slopes (all P for age by sex interaction <0.05). The blood pressures, body mass index (BMI), and triglyceride levels were identified as major clinical determinants of carotid properties with adjustment of age and sex. Conclusions::The age- and sex-specific reference values of carotid properties measured by RF ultrasound for healthy Chinese adults were established. The blood pressures, BMI, and triglyceride levels should be considered for clinical application of corresponding reference values.