ObjectiveAs a common lifestyle habit, alcohol consumption has a controversial association with the onset of Parkinson’s disease (PD). To demonstrate the correlation between alcohol consumption and PD and to identify associated genes, we integrated findings from clinical surveys, genomics, transcriptomics, and animal experiments. MethodsWe investigated the alcohol consumption rates (including both before and after disease onset) among 244 PD patients in China and 177 PD patients from the U.S. NHANES database. Mendelian randomization (MR) analysis was performed using genome-wide association study (GWAS) data for three alcohol-related traits and seven PD-related datasets from the MRC IEU OpenGWAS database. Transcriptomic data from the substantia nigra of PD patients were obtained from three GEO datasets (GSE7621, GSE20141, and GSE49036) to analyze RIT2 gene transcription. Finally, three groups of animal experiments (water/20% ethanol/20% liquor, with 4 C57BL/6J mice per group) were conducted to examine changes in brain RIT2 gene expression and transcriptomic profiles following alcohol consumption. ResultsThe alcohol consumption rates among PD patients in China and the U.S. (9%-18.87%) were significantly lower than the general population rates of 15%-45% in their respective regions (P<0.001), suggesting a possible negative association between alcohol consumption and PD. Subsequently, in 21 bidirectional MR analyses using 3 alcohol-related GWAS datasets and 7 PD-related GWAS datasets, the forward MR analyses (alcohol intake as exposure, PD as outcome) yielded 12 negative associations (OR_IVW<1) and 9 positive associations (OR_IVW>1). Among these, only two negative associations reached statistical significance: alcohol intake frequency (OR_IVW=0.75, 95% CI: 0.60-0.93, P=0.010) and alcohol consumption (OR_IVW=0.20, 95% CI: 0.05-0.83, P=0.026). The forward MR analysis (alcohol intake→PD) identified 235 SNPs, annotated to 316 genes, while the reverse MR analyses (PD→alcohol intake) identified 37 SNPs, annotated to 53 genes. Notably, only the RIT2 gene appeared in both the forward and reverse MR analyses (alcohol intake→PD: rs28597806, rs8083110; PD→alcohol intake: rs4588066). RIT2 is selectively expressed in the human brain (FPKM: 5.259±2.103), with low or no expression in peripheral tissues (FPKM: <1). Analysis of three human substantia nigra transcriptomic datasets revealed a decreasing trend in RIT2 gene expression in PD patients (GSE20141 array signal: 3.49±1.23 vs. 2.33±0.87, P=0.044). Animal experiments demonstrated that administration of 20% ethanol or 20% liquor (approximately 8% ethanol) stimulated a >2-fold upregulation of RIT2 gene expression in the mouse brain. Furthermore, transcriptomic sequencing revealed that the two alcohol-treated groups exhibited 96 (20% ethanol vs. water control) and 4 (20% liquor vs. water control) differentially expressed genes, respectively, indicating that low-dose alcohol consumption can achieve RIT2 upregulation while minimizing impact on other brain genes. In addition to its anti-infective effects, low-dose alcohol consumption primarily influences signaling pathways related to neurodegenerative diseases such as PD and Prion diseases. ConclusionAlcohol consumption is generally considered as a harmful lifestyle habit. However, some studies have also shown a lower risk of mortality among individuals who consume low doses of alcohol (100 g/week of ethanol) or drink occasionally. Currently, one of the research focuses on alcohol consumption is whether the human body can benefit from low-dose alcohol intake. This study provides new evidence supporting a negative association between alcohol consumption and PD, and for the first time, through MR analysis, identifies the RIT2 gene as a potential mediator of the effect of alcohol consumption on PD. RIT2 is selectively expressed in the human brain. Building upon existing evidence indicating downregulated RIT2 gene expression in PD pathogenesis, our experiments confirm that low-dose alcohol consumption can upregulate RIT2 expression in the brain. In brief, alcohol consumption may suppress the pathogenesis of PD by upregulating RIT2 expression in the substantia nigra. China is facing a serious problem of population aging. This study offers important insights for long-term PD prevention and treatment strategies, with the aim of benefiting more potential PD patients through lifestyle modifications, thereby improving the quality of life of the aging population and reducing the economic burden on healthcare.

Objective To analyze the infection status and epidemic trends of soil-borne nematode infections in Yubei, Chongqing City, in 2010,2021, and 2022. Methods The local populations from four survey sites of four towns in 2010 and five sites of five towns in 2021 and 2022 were surveyed regarding their basic information using a unified form. Fecal samples of the participants were collected and tested for soil-borne nematode infections using the modified Kato-Katz thick smear method. Results In 2010, 2 049 participants were surveyed, followed by 1 000 participants in 2021 and 2022. The overall prevalence of parasitic infections declined significantly from 3.86% to 0.20%. In 2010, soil-transmitted nematode included hookworms(3.81%) and roundworms(0.29%). In 2021, the infection rates of roundworms and hookworms were 1.70% and 0.10% respectively. Notably, only Ascaris was identified in 2022(0.20%). The≥60 age group consistently exhibited the highest infection rates across all surveys, followed by the 40-59 age group. The infection rates of males in the three surveys were 3.31%,1.92%, and 0.20% respectively, and those of females were 4.37%, 1.46%, and 0.20% respectively. There was no statistically significant difference in the infection rates between males and females. Educational attainment was inversely associated with infection; in 2010, the highest prevalence was observed among those with primary education or below, whereas in 2021, illiterate or semi-literate individuals showed the highest susceptibility. The occupational distribution of infections in 2010 indicated that retirees (8.33%), farmers(4.86%), and homemakers or unemployed individuals(3.45%)were the most affected. However, in 2021 and 2022, farmers emerged as the predominant occupational group with soil-transmitted nematode infections. Conclusions The infection rate of soil-borne nematodes showed a decreasing trend in Yubei, and the infection species changed from hookworms in 2010 to Ascaris in 2022. Farmers, the elderly, and people with low education levels should continue to be the focus of preventive and control efforts.

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 analyze the predictive factors associated with late-onset sepsis(LOS) in very low birth weight infants,and to develop a risk prediction score scale applicable to these infants three days postnatal.This will provide valuable insights for early diagnosis and timely intervention.Methods:Very low birth weight infants admitted to the Children's Hospital of Fudan University from January 1,2022,to June 30,2024,were selected as research subjects.These infants were categorized into two groups:the LOS group and the non-LOS group,based on whether they developed LOS.LASSO regression analysis,alongside univariate and multivariate regression analyses,was employed to identify predictive factors for LOS in this population.A Logistic model was constructed using the optimal combination of predictive variables,and a risk assessment scale was subsequently developed.The prediction performance of the model was evaluated using the Hosmer-Lemeshow chi-square test and the receiver operating characteristic curve.Results:A total of 444 cases of very low birth weight infants were included,of which 185 had LOS and 259 did not.After screening the variables,seven independent factors were included into the model:birth weight,gestational age,tracheal intubation,abnormal skin color,abdominal distension,elevated C-reactive protein levels,and right hand perfusion index.A predictive scoring scale was developed based on the regression coefficients of each variable,with corresponding risk scores assigned as follows:1,4,3,2,1,1,and 2; a score of ≥3.5 indicated high-risk groups.The Hosmer-Lemeshow test results demonstrated that χ2 = 7.602( P = 0.473).The area under the receiver operating characteristic curve was 0.792 ( P<0.001),with a sensitivity of 73.5% and specificity of 71.0%. Conclusion:The risk score scale developed in this study exhibits significant predictive capability,providing valuable insights for clinical medical personnel to assess the risk of LOS in very low birth weight infants during the early postnatal period.

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: The objective of our study was to evaluate the vaccine effectiveness (VE) of the pentavalent rotavirus vaccine (RV5) among < 5-year-old children in three provinces of China during 2020-2024 via a propensity score-matched test-negative case-control study. METHODS: Electronic health records and immunization information systems were used to obtain data on acute gastroenteritis (AGE) cases tested for rotavirus (RV) infection. RV-positive cases were propensity score matched with RV-negative controls for age, visit month, and province. RESULTS: The study included 27,472 children with AGE aged 8 weeks to 4 years at the time of AGE diagnosis; 7.98% (2,192) were RV-positive. The VE (95% confidence interval, CI) of 1-2 and 3 doses of RV5 against any medically attended RV infection (inpatient or outpatient) was 57.6% (39.8%, 70.2%) and 67.2% (60.3%, 72.9%), respectively. Among children who received the 3rd dose before turning 5 months of age, 3-dose VE decreased from 70.4% (53.9%, 81.1%) (< 5 months since the 3rd dose) to 63.0% (49.1%, 73.0%) (≥ 1 year since the 3rd dose). The three-dose VE rate was 69.4% (41.3%, 84.0%) for RVGE hospitalization and 57.5% (38.9%, 70.5%) for outpatient-only medically attended RVGE. CONCLUSION Three-dose RV5 VE against rotavirus gastroenteritis (RVGE) in children aged < 5 years was higher than 1-2-dose VE. Three-dose VE decreased with time since the 3rd dose in children who received the 3rd dose before turning five months of age, but remained above 60% for at least one year. VE was higher for RVGE hospitalizations than for medically attended outpatient visits.
Humans ; Rotavirus Vaccines/immunology* ; China/epidemiology* ; Case-Control Studies ; Child, Preschool ; Infant ; Rotavirus Infections/epidemiology* ; Male ; Propensity Score ; Female ; Vaccine Efficacy ; Gastroenteritis/virology* ; Vaccines, Attenuated ; Rotavirus

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult