A quantitative analytical method was established for 275 kinds of metabolites in plasma based on ultra-high performance liquid chromatography-triple quadrupole mass spectrometry/multiple reaction monitoring-selected ion monitoring(UHPLC-QQQ-MS/MRM-SIM)technique.Based on UHPLC-high resolution(HRMS)/full MS-parallel reaction monitoring(PRM)technique,165 kinds of lipid metabolites in plasma were qualitatively identified and quantitatively detected to address the analytical challenges of lipid isomers.This quantitative metabolomics approach was successfully applied to a rat myocardial infarction study,identifying and quantifying 372 kinds of metabolites in total.Comparative analysis with the sham-operated group revealed significant alterations in 59 kinds of plasma metabolites in myocardial infarction rats.Key metabolic pathways,which included amino acid metabolism(alanine,aspartate,and glutamate metabolism;glycine,serine,and threonine metabolism,etc.),one-carbon unit metabolism,and tricarboxylic acid cycle,were significantly disrupted.

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.

Recent studies have shown that the physiological homeostasis of oral mucosal cells is regulated by the circadian clock.Dis-ruption or dysfunction of the circadian clock is closely associated with the development of oral squamous cell carcinoma(OSCC).Research based on the circadian clock offers a novel perspective on the pathogenesis and therapeutic strategies for OSCC.However,there is current-ly limited research on this topic,and people generally have insufficient understanding and recognition of the circadian clock.Given the complexity and challenges of circadian clock which is the fourth dimension of medical research,we organize relevant experts based on summarizing the current research results of circadian clock in the pathogenesis and precision diagnosis and treatment of OSCC,combining the scientific principles of the circadian clock's role and their long-term research experience,then summarizes and recommends the con-sensus opinions for the research of circadian clock in the pathogenesis mechanism and precision diagnosis and treatment of human OSCC,with the hope of providing guidance for the basic research and clinical application of circadian clock or circadian rhythm in the pathogene-sis mechanism and precision diagnosis and treatment of oral and maxillofacial squamous cell carcinoma.

Objective:To observe the effect of high-frequency repetitive transcranial magnetic stimulation (rTMS) at different frequencies on cognitive impairment due to high-altitude hypoxia.Methods:Sixty officers and soldiers displaying cognitive impairment in a hypoxic high-altitude environment were randomly divided into 15Hz, 20Hz and 25Hz groups, each of 20. They were given rTMS at those frequencies for 30 days. Before the stimulation and after 15 and 30 days, event-related potentials, latencies of mismatched negativity (MMN) and P300 signals were recorded. The participants′ cognition was also evaluated using the Montreal Cognitive Assessment Scale (MoCA). Correlation between the electrophysiological indexes and the MoCA scores was computed.Results:After 15 days, all had shorter MMN latencies, higher total MoCA scores and better memory scores. The only significant difference among the three groups was in the average memory scores. After 15 days, MMN latency was significantly negatively correlated with the memory scores in all three groups ( r=0.44 to -0.54). Conclusions:rTMS at frequencies above 15Hz can effectively relieve cognitive impairment, especially memory dysfunction, resulting from high-altitude hypoxia.

Objective:To analyze the epidemiological characteristics and influencing factors of single-cigarette use and dual cigarette-cigar use in China.Methods:This study was a cross-sectional study that selected 85 638 urban and rural residents who met the inclusion criteria from the 2018 China Health Literacy Survey as research subjects. An analysis was conducted on 21 849 users of cigarettes and cigars among them. Due to the small number of individuals who exclusively used cigars (247 cases), the research subjects were divided into two categories: exclusive cigarette users and dual users of cigarettes and cigars. The groups were categorized by age (18-34 years, 35-54 years, ≥55 years), gender (male, female), education level (primary school and below, junior high school and high school, university and above) and annual household income (<20 000 yuan, 20 000-<80 000 yuan, ≥80 000 yuan) to compare the tobacco usage rate and conduct subgroup analyses for each subgroup. Multivariate logistic regression analysis was employed, incorporating general demographic characteristic information to explore the influencing factors of exclusive cigarette use and dual use of cigarettes and cigars, respectively.Results:The rate of exclusive cigarette use in our country was 24.3%, while the dual use rate of cigarettes and cigars was 0.9%. The exclusive cigarette use rate and the dual use rate of cigarettes and cigars among males were significantly higher than those among females (48.25% vs 2.48%, and 1.84% vs 0.06%) (both P<0.001). For males, the high-risk factors for exclusive cigarette use included living in urban areas ( OR: 1.37, 95% CI: 1.23-1.54), being Han ethnicity ( OR: 1.73, 95% CI: 1.51-1.98), and having an annual household income ≥20 000 yuan ( OR: 1.54, 95% CI: 1.38-1.82) while having a junior high school education or higher was a protective factor ( OR: 0.68, 95% CI: 0.52-0.90). Age≥35 years ( OR: 3.36, 95% CI: 2.62-4.32) and having a junior high school education or higher ( OR: 1.30, 95% CI: 1.02-1.67) were risk factors for dual use of cigarettes and cigars in males. Among females, living in urban areas ( OR: 1.53, 95% CI: 1.19-1.97) and being Han ethnicity ( OR: 5.96, 95% CI: 4.47-7.96) were risk factors for exclusive cigarette use, while having a university education or higher was a protective factor ( OR: 0.28, 95% CI: 0.18-0.42). However, for female dual use of cigarettes and cigars, no significant effects were observed for any demographic characteristics. Conclusions:The use rate of cigarettes alone in China is significantly higher than that of cigarette-cigar dual use, and the rates of cigarette use alone and cigarette-cigar dual use in men are significantly higher than those in women. Tobacco use is being affected by sociodemographic factors, among which place of residence, ethnicity and education level are the main influencing factors of cigarette use alone, and gender, age and education level are the main influencing factors of cigarette-cigar dual use.

Objective To investigate and compare the epidemiological characteristics of common respiratory viruses among influenza-like illness(ILI)and severe acute respiratory infection(SARI)cases in Huzhou,Zhejiang Province before and after the COVID-19 pandemic,so as to provide a basis for formulating and adjusting the prevention and control strategies for viral respiratory infectious diseases.Methods ILI and SARI cases at two influenza surveillance sentinel hospitals in Huzhou and had throat swab samples collected during Nov 2017 to Feb 2020(pre-COVID-19 pandemic period)and Dec 2022 to Apr 2024(post-COVID-19 mitigation phase)were selected as the participants.Seven common viral respiratory pathogens were tested,including influenza A virus(H1N1 and H3N2 subtypes),influenza B virus(Victoria lineage,FluB),respiratory syncytial virus(RSV),rhinovirus(HRV),adenovirus(ADV),and severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).The positive rates of respiratory pathogens before and after the COVID-19 pandemic were compared across different age groups and different time.Results A total of 7 948 ILI samples and 2 294 SARI samples were included.The overall positive rate of ILI samples increased from 33.6%to 47.1%,primarily due to the increase in influenza and COVID-19 infections;the overall positive rate of SARI samples decreased from 31.4%to 24.8%,mainly due to the reduction in HRV and ADV infections.During the post-COVID-19 mitigation phase,SARS-CoV-2(22.1%),H3N2(12.7%),and FluB(6.0%)were the primary pathogens in ILI samples,while RSV(7.1%),H3N2(5.3%),and HRV(4.5%)dominated in SARI samples.During the post-COVID-19 mitigation phase,the influenza virus circulation period was shortened.Before the COVID-19 pandemic,RSV was mainly detected in autumn and winter,while during the post-COVID-19 mitigation phase,out-of-season RSV epidemics were observed in spring and summer.Co-infection rate in ILI cases increased significantly in the post-COVID-19 mitigation phase,predominantly consisting of co-infections of COVID-19 and influenza A virus,while co-infection rate in SARI cases showed a decline.Conclusion We found important epidemiological changes in respiratory viruses in Huzhou during the post-COVID-19 mitigation phase compared to pre-COVID-19 period,including increased positive rates of influenza and COVID-19,and disruptions to the seasonal patterns of influenza and RSV.The prevention and control strategies should be adjusted in a timely manner based on the monitoring data.

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.

OBJECTIVES: To study the incidence and disease burden of congenital birth defects in children under five in China from 1990 to 2021 and to predict the incidence of congenital birth defects in this population from 2022 to 2036, providing a reference for the prevention of congenital birth defects in children. METHODS: Using the Global Burden of Disease Study 2021 (GBD 2021) database, the incidence and disability-adjusted life years (DALY) were employed to describe the disease burden. The Joinpoint regression model was used to analyze the trends in incidence and DALY rates of congenital birth defects in children under five. A grey prediction model GM(1,1) was applied to fit the trend of incidence rates of congenital birth defects in this age group and to predict the incidence from 2022 to 2036. RESULTS: In 2021, the incidence rate of congenital birth defects among children under five in China was 737.28 per 100 000. Among these, congenital musculoskeletal and limb deformities had the highest incidence rate at 307.15 per 100 000, followed by congenital heart defects (223.53 per 100 000), congenital urinary and genital tract malformations (74.99 per 100 000), and congenital gastrointestinal malformations (62.61 per 100 000). From 1990 to 2021, the incidence rate and DALY rate of congenital birth defects in children under five in China decreased at an average annual rate of 1.73% and 5.42%, respectively. The prediction analysis indicated a decreasing trend in the incidence of congenital birth defects among children under five in China from 2022 to 2036, with the incidence rate dropping from 892.36 per 100 000 in 2022 to 783.35 per 100 000 in 2036. CONCLUSIONS The incidence and disease burden of congenital birth defects in children under five in China showed a significant declining trend from 1990 to 2021. It is predicted that this incidence will continue to decrease until 2036.
Humans ; Congenital Abnormalities/epidemiology* ; China/epidemiology* ; Incidence ; Infant ; Infant, Newborn ; Child, Preschool ; Female ; Male ; Forecasting ; Disability-Adjusted Life Years