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 explore the potential mechanism of proguanil on the proliferation and apoptosis of bladder cancer cells.Methods 253J cells were randomly divided into control group(normal treatment),proguanil group(42.06 μmol·L-1 proguanil),pcDNA group(transfected with pcDNA+42.06 μmol·L-1 proguanil),FADS2 group[transfected fatty acid desaturase gene 2(FADS2)+42.06 μmol·L-1 proguanil],si-NC(transfection si-NC),si-FADS2(transfection si-FADS2),Ferrostatin-1 group(transfected with si-FADS2+10 μmol·L-1 ferrostatin-1).Real-time fluorescence quantitative polymerase chain reaction(RT-qPCR)assay was used to detect mRNA expression of related genes;Western blot assay was used to detect the expression of each protein;apoptosis was detected by TdT mediated dUDP nick end labeling(Tunel)assay;5-ethynyl-2'-deoxyuridine(EdU)assay to detect cell proliferation;the Transwell assay measures the ability of cells to migrate;Fe2+levels were determined by kit method;DCFH-DA probe was used to detect ROS levels.Results The mRNA levels of FADS2 in control group,proguanil group,pcDNA group and FADS2 group were 1.00±0.11,0.47±0.09,0.49±0.06 and 2.09±0.21,respectively;cell proliferation rate were(100.00±3.50)％,(54.31±4.90)％,(56.46±5.17)％and(78.76±6.50)％,respectively;the apoptosis rate were(3.92±0.53)％,(28.79±3.30)％,(27.20±2.90)％and(7.34±0.68)％,respectively;the migration number were 132.70±9.81,70.10±5.05,68.70±537 and 101.80±11.25,respectively;Fe2+level were(100.00±8.14)％,(201.33±17.84)％,(192.38±21.34)％and(116.70±10.90)％,respectively;GPX4 protein relative expression level were 0.77±0.05,0.31±0.05,0.34±0.05 and 0.68±0.06,respectively.The above indexes in proguanil group were compared with those in control group,the above indexes in FADS2 group were compared with those in pcDNA group,all the differences were statistically significant(all P＜0.05).The ROS levels of si-NC,si-FADS2 and Ferrostatin-1 groups were 9.72±1.18,40.94±5.63 and 13.77±1.40,respectively.Compared the si-FADS2 group with the si-NC group,Ferrostatin-1 group compared with si-FADS2 group,ROS level were significantly different(all P＜0.05).Conclusion Proguanil can induce the apoptosis of bladder cancer cells by inhibiting FADS2 expression mediated by oxidation-reduction driven ferroptosis pathway.

BACKGROUND: Controversy exists as to the optimal treatment approach for ostial left anterior descending (LAD) or ostial left circumflex artery (LCx) lesions. Drug-coated balloons (DCB) may overcome some of the limitations of drug-eluting stents (DES). Therefore, we investigated the security and feasibility of the DCB policy in patients with ostial LAD or ostial LCx lesions, and compared it with the conventional DES-only strategy. METHODS: We retrospectively enrolled patients with de novo ostial lesions in the LAD or LCx who underwent interventional treatment. They were categorized into two groups based on their treatment approach: the DCB group and the DES group. The treatment strategies in the DCB group involved the use of either DCB-only or hybrid strategies, whereas the DES group utilized crossover or precise stenting techniques. Two-year target lesion revascularization was the primary endpoint, while the rates of major adverse cardiovascular events, cardiac death, target vessel myocardial infarction, and vessel thrombosis were the secondary endpoints. Using propensity score matching, we assembled a cohort with comparable baseline characteristics. To ensure result analysis reliability, we conducted sensitivity analyses, including interaction, and stratified analyses. RESULTS: Among the 397 eligible patients, 6.25% of patients who were planned to undergo DCB underwent DES. A total of 108 patients in each group had comparable propensity scores and were included in the analysis. Two-year target lesion revascularization occurred in 5 patients (4.90%) and 16 patients (16.33%) in the DCB group and the DES group, respectively (odds ratio = 0.264, 95% CI: 0.093-0.752, P = 0.008). Compared with the DES group, the DCB group demonstrated a lower major adverse cardiovascular events rate (7.84% vs. 19.39%, P = 0.017). However, differences with regard to cardiac death, non-periprocedural target vessel myocardial infarction, and definite or probable vessel thrombosis between the groups were non-significant. CONCLUSIONS The utilization of the DCB approach signifies an innovative and discretionary strategy for managing isolated ostial lesions in the LAD or LCx. Nevertheless, a future randomized trial investigating the feasibility and safety of DCB compared to the DES-only strategy specifically for de novo ostial lesions in the LAD or LCx is highly warranted.

OBJECTIVE: To evaluate the associations of lipid indicators and mortality in Beijing Elderly Comprehensive Health Cohort Study. METHODS: A prospective cohort was conducted based on Beijing Elderly Comprehensive Health Cohort Study with 4499 community older adults. After the baseline survey, the last follow-up was March 31, 2021 with an average 8.13 years of follow-up. Cox proportional hazard model was used to estimate the hazard ratios (HR) with 95% CI for cardiovascular disease (CVD) death and all-cause death in associations with baseline lipid indicators. RESULTS: A total of 4499 participants were recruited, and the mean levels of uric acid, body mass index, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, total cholesterol (TC), triglyceride, and low-density lipoprotein cholesterol (LDL-C) showed an upward trend with the increasing remnant cholesterol (RC) quarters (P_trend < 0.05), while the downward trend was found in high-density lipoprotein cholesterol (HDL-C). During the total 36,596 person-years follow-up, the CVD mortality and all-cause mortality during an average 8.13 years of follow-up was 3.87% (95% CI: 3.30%-4.43%) and 14.83% (95% CI: 13.79%-15.86%) with 174 CVD death participants and 667 all-cause death participants. After adjusting for confounders, the higher level of TC (HR = 0.854, 95% CI: 0.730-0.997), LDL-C (HR = 0.817, 95% CI: 0.680-0.982) and HDL-C (HR = 0.443, 95% CI: 0.271-0.724) were associated with lower risk of CVD death, and the higher level of HDL-C (HR = 0.637, 95% CI: 0.501-0.810) were associated with lower risk of all-cause death. The higher level of RC (HR = 1.276, 95% CI: 1.010-1.613) increase the risk of CVD death. Compared with the normal lipid group, TC ≥ 6.20 mmol/L group and LDL-C ≥ 4.10 mmol/L group were no longer associated with lower risk of CVD death, while RC ≥ 0.80 mmol/L group was still associated with higher risk of CVD death. In normal lipid group, the higher levels of TC, LDL-C and HDL-C were related with lower CVD death. CONCLUSIONS In community older adults, higher levels of TC and HDL-C were associated with lower CVD mortality in normal lipid reference range. Higher RC was associated with higher CVD mortality, which may be a better lipid indicator for estimating the CVD death risk in older adults.

Objective: To investigate the distribution of blood pressure and analyze the associated factors of blood pressure of the elderly with type 2 diabetes in Jiangsu Province. Methods: The elderly over 60 years old participants with type 2 diabetes in the communities of Huai'an City and Changshu City, Jiangsu Province were selected in this study. They were divided into two groups: taking antihypertensive drugs and not taking antihypertensive drugs. The demographic characteristics, such as age and sex, and relevant factors were collected by questionnaire. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by physical examination. The percentile of SBP and DBP in each age group of men and women were described. The kernel density estimation curve was used to show the blood pressure distribution. The trend of blood pressure with age was fitted by locally weighted regression. The logistic regression model was used to analyze relevant factors of blood pressure. Results: A total of 12 949 participants were included in this study, including 7 775 patients in the antihypertensive drug group and 5 174 patients in the group without antihypertensive drugs. The SBP of participants was concentrated at 140-160 mmHg, and their DBP was concentrated at 75-85 mmHg. There were significant differences in the distribution of blood pressure among the subgroups of body mass index (BMI) and rural areas whether taking antihypertensive drugs and not. For participants aged under 80 years old, the SBP showed an increasing trend with age and the DBP showed a decreasing trend with age. Age, BMI ≥24 kg/m², fasting blood glucose ≥7.0 mmol/L, living in rural areas and no smoking were influencing factors of the elevated SBP; BMI ≥24 kg/m², male, living in rural areas, no smoking, drinking alcohol and not receiving drug hypoglycemic treatment were influencing factors of the elevated DBP. Conclusion: The SBP of older diabetic adults in Jiangsu Province is at a high level, and the distribution of blood pressure is significantly different between men and women in taking antihypertensive drugs group. The SBP presents a rising trend and the DBP is decreasing at the age of 60-80 years. The blood pressure level of this population are mainly affected by age, BMI, urban and rural areas, smoking.
Adult ; Aged ; Humans ; Male ; Female ; Middle Aged ; Aged, 80 and over ; Blood Pressure/physiology* ; Diabetes Mellitus, Type 2/epidemiology* ; Antihypertensive Agents/therapeutic use* ; Smoking ; Body Mass Index ; Hypertension/epidemiology*

Objective: To analyze the prevalence and the risk factors of fungal sepsis in 25 neonatal intensive care units (NICU) among preterm infants in China, and to provide a basis for preventive strategies of fungal sepsis. Methods: This was a second-analysis of the data from the "reduction of infection in neonatal intensive care units using the evidence-based practice for improving quality" study. The current status of fungal sepsis of the 24 731 preterm infants with the gestational age of <34⁺⁰ weeks, who were admitted to 25 participating NICU within 7 days of birth between May 2015 and April 2018 were retrospectively analyzed. These preterm infants were divided into the fungal sepsis group and the without fungal sepsis group according to whether they developed fungal sepsis to analyze the incidences and the microbiology of fungal sepsis. Chi-square test was used to compare the incidences of fungal sepsis in preterm infants with different gestational ages and birth weights and in different NICU. Multivariate Logistic regression analysis was used to study the outcomes of preterm infants with fungal sepsis, which were further compared with those of preterm infants without fungal sepsis. The 144 preterm infants in the fungal sepsis group were matched with 288 preterm infants in the non-fungal sepsis group by propensity score-matched method. Univariate and multivariate Logistic regression analysis were used to analyze the risk factors of fungal sepsis. Results: In all, 166 (0.7%) of the 24 731 preterm infants developed fungal sepsis, with the gestational age of (29.7±2.0) weeks and the birth weight of (1 300±293) g. The incidence of fungal sepsis increased with decreasing gestational age and birth weight (both P<0.001). The preterm infants with gestational age of <32 weeks accounted for 87.3% (145/166). The incidence of fungal sepsis was 1.0% (117/11 438) in very preterm infants and 2.0% (28/1 401) in extremely preterm infants, and was 1.3% (103/8 060) in very low birth weight infants and 1.7% (21/1 211) in extremely low birth weight infants, respectively. There was no fungal sepsis in 3 NICU, and the incidences in the other 22 NICU ranged from 0.7% (10/1 397) to 2.9% (21/724), with significant statistical difference (P<0.001). The pathogens were mainly Candida (150/166, 90.4%), including 59 cases of Candida albicans and 91 cases of non-Candida albicans, of which Candida parapsilosis was the most common (41 cases). Fungal sepsis was independently associated with increased risk of moderate to severe bronchopulmonary dysplasia (BPD) (adjusted OR 1.52, 95%CI 1.04-2.22, P=0.030) and severe retinopathy of prematurity (ROP) (adjusted OR 2.55, 95%CI 1.12-5.80, P=0.025). Previous broad spectrum antibiotics exposure (adjusted OR=2.50, 95%CI 1.50-4.17, P<0.001), prolonged use of central line (adjusted OR=1.05, 95%CI 1.03-1.08, P<0.001) and previous total parenteral nutrition (TPN) duration (adjusted OR=1.04, 95%CI 1.02-1.06, P<0.001) were all independently associated with increasing risk of fungal sepsis. Conclusions: Candida albicans and Candida parapsilosis are the main pathogens of fungal sepsis among preterm infants in Chinese NICU. Preterm infants with fungal sepsis are at increased risk of moderate to severe BPD and severe ROP. Previous broad spectrum antibiotics exposure, prolonged use of central line and prolonged duration of TPN will increase the risk of fungal sepsis. Ongoing initiatives are needed to reduce fungal sepsis based on these risk factors.
Infant ; Infant, Newborn ; Humans ; Birth Weight ; Intensive Care Units, Neonatal ; Retrospective Studies ; Tertiary Care Centers ; Infant, Extremely Low Birth Weight ; Gestational Age ; Infant, Extremely Premature ; Sepsis/epidemiology* ; Retinopathy of Prematurity/epidemiology* ; Bronchopulmonary Dysplasia/epidemiology*