Objective: To explore the performance of machine learning prediction models in forecasting student absenteeism due to respiratory symptoms caused by air pollution in short term, aiming to provide a methodological reference for early warning systems of school diseases. Methods: Utilizing data from shortterm sequences of student absenteeism due to respiratory symptoms in Jiangsu Province from September 2019 to October 2022, the study integrated average concentrations of atmospheric pollutants. A univariate distributed lag nonlinear model was employed to select optimal lag variables for the pollutants. An extreme gradient boosting(XGBoost) algorithm model was developed to predict the frequency of absenteeism due to respiratory symptoms and compared with the seasonal autoregressive integrated moving average with exogenous factors(SARIMAX) model. Results: Between 2019 and 2022, an average of 9 709 students per day in Jiangsu Province were absent due to respiratory symptoms. The daily average air quality index (AQI) was 76.96,with mass concentrations of PM2.5, PM10, NO2, and O3 averaging at 35.75, 61.13, 28.89, 104.81 μg/m3, respectively. Granger causality tests indicated that AQI, PM2.5, PM10, NO2, and O3 were significant predictors of absenteeism frequency due to respirutory symptoms(F=1.46,1.79,1.67,3.41,2.18,P<0.01). The singleday lag effects of PM2.5, PM10, NO2, and O3 reached their peak relative risk (RR) values at lag4, lag0, lag0, lag4 respectively. When integrating these optimal lag variables for the pollutants, the XGBoost model demonstrated superior predictive performance to the SARIMAX model, reducing the mean absolute error (MAE) from 2.251 to 0.475, mean absolute percentage error (MAPE) from 0.429 to 0.080, and root mean square error (RMSE) from 2.582 to 0.713; at the P75 percentile alert threshold, the sensitivity improved from 0.086 to 0.694 and specificity from 0.979 to 0.988, with the Youden index increasing from 0.065 to 0.682. Conclusions The XGBoost model exhibits robust predictive performance and effective early warning capabilities for shortterm sequences of student absenteeism due to respiratory symptoms caused by air pollution. Schools could timely adopt this model to preemptively detect and control disease outbreaks, thereby enhancing school health management.

Objective:To investigate the current situation and influence factors of prehospital thrombolysis treatment for ST segment elevation myocardial infarction (STEMI) in China, to analyze the main factors affecting prehospital thrombolysis implementation, and optimize the pre-hospital thrombolysis strategy for STEMI to reduce mortality.Methods:A multicenter cross-sectional survey was conducted. 21 cities from six major geographical regions in China were selected by using convenient sampling method. An anonymous online electronic questionnaire was used to investigate the current situation and influence factors of prehospital emergency physicians and grassroots physicians implementing prehospital thrombolysis treatment for STEMI patients. Chi-square test was used to analyze the differences in count data between groups, and multivariate logistic regression was used to analyze the factors affecting prehospital thrombolysis in STEMI.Results:A total of 5 163 prehospital emergency physicians and physicians from grassroots township health centers/community health service centers or village clinics participated in this survey. Among them, 3208 (62.13%) have never implemtent thrombolysis, and 1 955 (37.87%) have did it before. The results of the multivariate logistic regression analysis indicated that physicians with 5-10 years of experience ( OR=1.41, 95% CI: 1.18-1.69, P<0.01), 11-20 years of experience ( OR=1.25, 95% CI: 1.03-1.52, P=0.02), those working in village clinics ( OR=1.30, 95% CI: 1.05-1.61, P=0.02), those in pre-hospital emergency medical institutions/departments ( OR=3.19, 95% CI: 2.80-3.64, P<0.01), those whose units are equipped with remote ECG transmission capabilities ( OR=1.72, 95% CI: 1.50-1.96, P<0.01), or ECG AI-assisted diagnostic tools ( OR=1.31, 95% CI: 1.15-1.49, P<0.01), and those who believe that thrombolysis is highly effective and should be widely adopted ( OR=2.55, 95% CI: 2.09-3.12, P<0.01) or consider it somewhat effective but warranting caution ( OR=2.11, 95% CI: 1.73-2.59, P<0.001), were more likely to make pre-hospital thrombolysis decisions for STEMI patients. To improve the current situation of pre-hospital thrombolysis for STEMI, the top four measures prioritized by pre-hospital emergency and grassroots physicians were enhancing the rescue capabilities of primary care doctors (92.22%), strengthening guidance from higherlevel hospitals (84.99%), increasing support for information technology (83.37%), and improving public health education (74.75%). Conclusions:The implementation rate of prehospital thrombolysis for STEMI in China still needs to be improved. Optimizing the prehospital thrombolysis strategy for STEMI, strengthening the allocation of basic medical resources and information technology support, and improving the referral mechanism are conducive to the implementation of prehospital thrombolysis for STEMI.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To explore the relationship between androgen level and adverse pregnancy outcome of pregnant women at advanced maternal age.Methods A total of 192 pregnant women who were admitted to Changzhou Maternal and Child Health Care Hospital for delivery from May to October 2022 were selected as the study objects.According to guidelines for diagnosis and treatment of hypertensive disorder complicating pregnancy and maternal age,the study objects were divided into simple pregnant women at advanced maternal age group,pregnant women at advanced maternal age complicated with hypertensive disorder complicating pregnancy,healthy control group and age-appropriate pregnant women complicated with hypertensive disorder complicating pregnancy group.Serum levels of five androgens[total testosterone(TT),sex hormone binding globulin(SHBG),free testosterone index(FTI),dehydroepiandrosterone sulfate(DHEAS)and androstendi-one(A2)]in each group were detected by chemiluminescence method.Results Compared with the healthy control group,TT,A2,FTI were significantly increased and SHBG was significantly decreased in the age-ap-propriate pregnant women complicated with hypertensive disorder complicating pregnancy group,the level of DHEAS was decreased in the simple pregnant women at advanced maternal age group,and the differences were statistically significant(P＜0.05).Pearson correlation analysis showed that TT was negatively correla-ted with age(P＜0.05),positively correlated with systolic blood pressure,diastolic blood pressure and body mass index(P＜0.05),and had no correlation with offspring sex and offspring weight(P＞0.05).Multivari-ate Logistic regression analysis showed that TT and body mass index were independent risk factors for hyper-tensive disorder complicating pregnancy in pregnant women(P＜0.05).Conclusion The level of androgen in pregnant women at advanced maternal age is related to the occurrence of hypertensive disorder complicating pregnancy.

Objective To observe the effect of different concentrations of glutamine(Gln)on the radiosensitivity of colorectal cancer HT-29 cells and explore the possible mechanism.Methods According to different Gln concentrations,HT-29 cells at logarithmical growth were divided into control group(2 mmol/L,as the basal medium concentration group)and experimental groups Ⅰ,Ⅱ and Ⅲ(4,6 and 8 mmol/L).After a 2-hour pre-treatment,all groups were exposed to 8 Gy irradiation of a Co-60 radiation source.CCK-8 assay and clonal formation assay were used respectively to explore the effects of different Gln concentrations on cell viability and cell radiosensitivity after irradiation.The level of reactive oxygen species(ROS)in each group was measured in 24 h after irradiation,and the apoptotic rate was detected with flow cytometry in 48 h after irradiation.The protein expression levels of Nrf2,HO-1,and cleaved-Caspase3 were determined by Western blotting.Results In 24 h after Gln intervention,the cell viability of experimental groups Ⅱ and Ⅲof non-irradiated HT-29 cells was significantly higher than that of the control group and of experimental group Ⅰ(P＜0.05).In 24 h after radiation,the cell viability of each experimental group was significantly higher than that of the control group(P＜0.05).In 14 d after radiation,there were more clone formation in each experimental group than the control group(P＜0.05).The ROS level was significantly lower in each experimental group than the control group in 24 h after radiation(P＜0.05).After 48 h of radiation,the apoptotic rate was notably lower in each experimental group than the control group(P＜0.05).The expression level of Nrf2 in the experimental group Ⅰ was higher than that of the control group(P＜0.05),those of Nrf2 and HO-1 in the experimental groups Ⅱ and Ⅲ were higher than those of the control group and experimental group Ⅰ(P＜0.05).While the expression of cleaved-Caspase3 in the experimental groups Ⅱ and Ⅲ was lower than the control group and experimental group Ⅰ(P＜0.05),and it in the experimental group Ⅲ was lower than that of experimental group Ⅱ(P＜0.05).Conclusion Gln can significantly reduce the radiosensitivity of HT-29 cells,which is associated with its reducing oxidative stress damage and reducing cell apoptosis.Our results suggest that Gln might be detrimental to radiation therapy in patients with colorectal cancer.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Research integrity is the foundation for ensuring the sound and orderly development of scientific and technological innovation. As the main battlefield of clinical medical research, hospitals should effectively fulfill their main responsibilities and do a good job in research integrity management. The China Hospital Research Integrity Alliance, consisting of the first batch of 43 hospitals, was established in November 2021. With the aim of " complementary advantages, resource sharing, and collaborative development", the alliance has carried out construction practices from seven aspects: construction mode, cultural system construction, organizational management, institutional construction, publicity and education, early warning and supervision, and technological empowerment. It has achieved the overall improvement of the research integrity construction ability of member units of the alliance, organic linkage between government and medical institutions, and efficient combination of internal and external resources, which can provide reference for the research integrity construction of medical institutions in China.

Organ transplantation is the most effective treatment for all categories of end-stage organ diseases. To resolve the shortage of donors in organ transplantation, widespread attention has been diverted to xenotransplantation. At present, clinicians mainly highlight the problems related to xenotransplantation rejection and viral infection. The physiology of xenotransplantation has been rarely studied. Kidney performs endocrine function by producing erythropoietin (EPO), renin and activating vitamin D. Although these pathways are usually well preserved in allogeneic transplantation, species-specific differences, especially those between pigs and non-human primates, may still affect the physiological function of transplant organs. In this article, the changes of EPO, renin-angiotensin-aldosterone system (RAAS) and active vitamin D₃ of pig and human after xenotransplantation were illustrated, aiming to provide reference for subclinical research of xenotransplantation.

Humans ; Staphylococcus lugdunensis/genetics* ; Peptides, Cyclic ; Chromosomes ; Operon/genetics* ; Staphylococcal Infections/genetics* ; Bacterial Proteins/genetics* ; Anti-Bacterial Agents

BACKGROUND: China bears the biggest atrial fibrillation (AF) burden in the world. However, little is known about the incidence and predictors of AF. This study aimed to investigate the current incidence of AF and its electrocardiographic (ECG) predictors in general community individuals aged over 60 years in China. METHODS: This was a prospective cohort study, recruiting subjects who were aged over 60 years and underwent annual health checkups from April to July 2015 in four community health centers in Songjiang District, Shanghai, China. The subjects were then followed up from 2015 to 2019 annually. Data on sociodemographic characteristics, medical history, and the resting 12-lead ECG were collected. Kaplan-Meier curve was used for showing the trends in AF incidence and calculating the predictors of AF. Associations of ECG abnormalities and AF incidence were examined using Cox proportional hazard models. RESULTS: This study recruited 18,738 subjects, and 351 (1.87%) developed AF. The overall incidence rate of AF was 5.2/1000 person-years during an observation period of 67,704 person-years. Multivariable Cox regression analysis indicated age (hazard ratio [HR], 1.07; 95% confidence interval [CI]: 1.06-1.09; P < 0.001), male (HR, 1.30; 95% CI: 1.05-1.62; P = 0.018), a history of hypertension (HR, 1.55; 95% CI: 1.23-1.95; P < 0.001), a history of cardiac diseases (HR, 3.23; 95% CI: 2.34-4.45; P < 0.001), atrial premature complex (APC) (HR, 2.82; 95% CI: 2.17-3.68; P < 0.001), atrial flutter (HR, 18.68; 95% CI: 7.37-47.31; P < 0.001), junctional premature complex (JPC) (HR, 3.57; 95% CI: 1.59-8.02; P = 0.002), junctional rhythm (HR, 18.24; 95% CI: 5.83-57.07; P < 0.001), ventricular premature complex (VPC) (HR, 1.76; 95% CI: 1.13-2.75, P = 0.012), short PR interval (HR, 5.49; 95% CI: 1.36-22.19; P = 0.017), right atrial enlargement (HR, 6.22; 95% CI: 1.54-25.14; P = 0.010), and pacing rhythm (HR, 3.99; 95% CI: 1.57-10.14; P = 0.004) were independently associated with the incidence of AF. CONCLUSIONS The present incidence of AF was 5.2/1000 person-years in the studied population aged over 60 years in China. Among various ECG abnormalities, only APC, atrial flutter, JPC, junctional rhythm, short PR interval, VPC, right atrial enlargement, and pacing rhythm were independently associated with AF incidence.
Humans ; Male ; Middle Aged ; Aged ; Atrial Fibrillation/epidemiology* ; Prospective Studies ; Incidence ; Atrial Flutter/complications* ; Risk Factors ; China/epidemiology* ; Electrocardiography