To understand the pathogenicity and drug resistance of swine-derived E.coli and its bio-logical characteristics in some areas in Hebei,E.coli was isolated and identified from diarrheal fe-ces of piglets collected from swine farms,and the isolated strains were subjected to drug sensitivity test,detection of the ability to form biofilm,pathogenicity test,virulence gene test,drug resistance gene test,and identification of phylogenetic subgroups.The results showed that a total of 35 patho-genic E.coli strains were isolated from the feces of diarrheic piglets,and most of the isolates were multidrug-resistant,and were resistant to at least three antibiotics,including amoxicillin(88.57％),ampicillin(88.57％),doxycycline(88.75％),sulfisoxazole(77.17％),lincomycin(100％),and chloramphenicol(100％);the isolates were severely resistant.The isolates all carried virulence genes,with a total of five virulence genes detected,namely,EAST1(77.14％),eaeA(17.14％),stx2e(5.71％),LT(2.86％),and STb(2.86％),and the isolates also carried multi-re-sistance genes,with a total of five virulence genes detected,namely,bla TEM-1(65.71％),bla CTX-M(20.00％),tetA(82.86％),tetB(14.29％),aadA2(17.14％),aac(6')-Ib(14.29％),qnrS(17.14％),sul 1(40.00％),sul2(34.29％),and floR(60.00％);the phylogenetic grouping showed that the isolates had a high proportion of group B1 and group A;and all 35 isolates showed differ-ent pathogenicity after infection of mice.This study provides a reference for the selection of effec-tive therapeutic drugs and the development of prevention and control programs for swine-origin pathogenic E.coli in Hebei Province.

Objective:To establish a quantitative analysis of multi-components by single marker(QAMS)for the determination of 6 alkaloid components,which is benzoylmesaconine,benzoyl-hypaconine,benzoylaconine,mesaconitine,hypaconitine,and aconitine in Zhachong Shisanwei Pills,and prove the scientificity and feasibility of the method in the quality analysis.Methods:The chromatographic separation was performed on an Agilent Eclipse Plus C18(250 mm×4.6 mm,5 μm)with gradient elution using 0.1 mol·L-1 ammonium acetate(0.5 mL of gla-cial acetic acid per 1 000 mL)(A)-acetonitrile:tetrahydrofuran(25∶15)(B),as the mobile phase(0-50 min,18%B-28%B),the detection wavelength was switched from 235 nm,the column temperature was kept at 40℃and the flow rate was 1.0 mL·min-1.The relative correction factors(fs/i)were established with the other 5 compo-nents to be measured using benzoylaconine as the internal reference,which were used to calculate the mass fraction of each component.At the same time,the mass fractions of the 6 effective constituents in Zhachong Shisanwei Pills were calculated by the external standard method(ESM).By comparing the content results of ESMand QAMS,the accura-cy of QAMS method were evaluated.Results:The relative correction factors(fs/i)of benzoylmesaconine,benzoylhyp-aconine,mesaconitine,hypaconitine,and aconitine in Mongolian medicine Zhachong Shisanwei Pills were reproduci-ble with good reproducibility,which were 0.680 4,0.450 6,0.850 8,0.676 1 and 0.757 0,the result obtained by QAMS approximated those obtained by external standard method(ESM).Conclusion:The method is simple,stable and reproducible,and can be used for the quality control of 6 alkaloid components in Zhachong Shisanwei Pills.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective:To analyze the effects of shift work on the renal function of oil workers and investigate whether there is a measured response relationship between shift work and renal dysfunction.Methods:In this study, oil workers who participated in physical examinations at the North China Oilfield Downhole Hospital were selected as the study subjects, and the physical examinations as well as questionnaires of the study subjects in 2017 and 2018 were collected as the baseline data, which included blood biochemical indexes, socio-demographic characteristics, history of life behaviors, occupational exposures, and occupational histories. Three follow-up surveys were subsequently conducted in April 2019, April 2020, and January 2021. The presence of renal dysfunction in the study population was determined based on the glomerular filtration rate tested at the medical examination hospital. The exposure of the study subjects to shift work was assessed using the weighted shift index (WSI), the relationship between different levels of shift work and renal dysfunction was analyzed using Cox regression, and the measure of WSI and renal dysfunction was explored by restricted cubic spline (RCS). response relationship.Results:A total of 2292 study participants were included in this study, and the prevalence density of renal dysfunction was 87.44 k/year, of which the prevalence of renal dysfunction in females (30.31%), those with per capita monthly income <2, 000 yuan (27.00%), those who consume alcohol (27.10%), those who are hypertensive (23.05%), those who are exposed to high temperatures (27.37%), those who are exposed to organic solvents (30.42%), and those who are engaged in shift work (25.87%) were to be found had a higher prevalence of renal dysfunction ( P<0.05). After correcting for age, sex, and other risk factors, there was a nonlinear association between intensity of shift work and renal dysfunction, with a hazard ratio ( HR) of 1.29 (95% CI: 0.98-1.59) for the development of renal dysfunction in petroleum workers for shift work performed at higher intensities, and moderate intensity of exposure to shift work reduced the risk of renal dysfunction in petroleum workers ( HR=0.54 with a 95% CI: 0.39-0.75, P<0.001) . Conclusion:Prolonged shift work increases the risk of renal dysfunction in oil workers, and the occurrence of renal dysfunction in oil workers is influenced by multiple factors.

Objective:To analyze the effects of shift work on the renal function of oil workers and investigate whether there is a measured response relationship between shift work and renal dysfunction.Methods:In this study, oil workers who participated in physical examinations at the North China Oilfield Downhole Hospital were selected as the study subjects, and the physical examinations as well as questionnaires of the study subjects in 2017 and 2018 were collected as the baseline data, which included blood biochemical indexes, socio-demographic characteristics, history of life behaviors, occupational exposures, and occupational histories. Three follow-up surveys were subsequently conducted in April 2019, April 2020, and January 2021. The presence of renal dysfunction in the study population was determined based on the glomerular filtration rate tested at the medical examination hospital. The exposure of the study subjects to shift work was assessed using the weighted shift index (WSI), the relationship between different levels of shift work and renal dysfunction was analyzed using Cox regression, and the measure of WSI and renal dysfunction was explored by restricted cubic spline (RCS). response relationship.Results:A total of 2292 study participants were included in this study, and the prevalence density of renal dysfunction was 87.44 k/year, of which the prevalence of renal dysfunction in females (30.31%), those with per capita monthly income <2, 000 yuan (27.00%), those who consume alcohol (27.10%), those who are hypertensive (23.05%), those who are exposed to high temperatures (27.37%), those who are exposed to organic solvents (30.42%), and those who are engaged in shift work (25.87%) were to be found had a higher prevalence of renal dysfunction ( P<0.05). After correcting for age, sex, and other risk factors, there was a nonlinear association between intensity of shift work and renal dysfunction, with a hazard ratio ( HR) of 1.29 (95% CI: 0.98-1.59) for the development of renal dysfunction in petroleum workers for shift work performed at higher intensities, and moderate intensity of exposure to shift work reduced the risk of renal dysfunction in petroleum workers ( HR=0.54 with a 95% CI: 0.39-0.75, P<0.001) . Conclusion:Prolonged shift work increases the risk of renal dysfunction in oil workers, and the occurrence of renal dysfunction in oil workers is influenced by multiple factors.

Objective:To establish a quantitative analysis of multi-components by single marker(QAMS)for the determination of 6 alkaloid components,which is benzoylmesaconine,benzoyl-hypaconine,benzoylaconine,mesaconitine,hypaconitine,and aconitine in Zhachong Shisanwei Pills,and prove the scientificity and feasibility of the method in the quality analysis.Methods:The chromatographic separation was performed on an Agilent Eclipse Plus C18(250 mm×4.6 mm,5 μm)with gradient elution using 0.1 mol·L-1 ammonium acetate(0.5 mL of gla-cial acetic acid per 1 000 mL)(A)-acetonitrile:tetrahydrofuran(25∶15)(B),as the mobile phase(0-50 min,18%B-28%B),the detection wavelength was switched from 235 nm,the column temperature was kept at 40℃and the flow rate was 1.0 mL·min-1.The relative correction factors(fs/i)were established with the other 5 compo-nents to be measured using benzoylaconine as the internal reference,which were used to calculate the mass fraction of each component.At the same time,the mass fractions of the 6 effective constituents in Zhachong Shisanwei Pills were calculated by the external standard method(ESM).By comparing the content results of ESMand QAMS,the accura-cy of QAMS method were evaluated.Results:The relative correction factors(fs/i)of benzoylmesaconine,benzoylhyp-aconine,mesaconitine,hypaconitine,and aconitine in Mongolian medicine Zhachong Shisanwei Pills were reproduci-ble with good reproducibility,which were 0.680 4,0.450 6,0.850 8,0.676 1 and 0.757 0,the result obtained by QAMS approximated those obtained by external standard method(ESM).Conclusion:The method is simple,stable and reproducible,and can be used for the quality control of 6 alkaloid components in Zhachong Shisanwei Pills.

To understand the pathogenicity and drug resistance of swine-derived E.coli and its bio-logical characteristics in some areas in Hebei,E.coli was isolated and identified from diarrheal fe-ces of piglets collected from swine farms,and the isolated strains were subjected to drug sensitivity test,detection of the ability to form biofilm,pathogenicity test,virulence gene test,drug resistance gene test,and identification of phylogenetic subgroups.The results showed that a total of 35 patho-genic E.coli strains were isolated from the feces of diarrheic piglets,and most of the isolates were multidrug-resistant,and were resistant to at least three antibiotics,including amoxicillin(88.57％),ampicillin(88.57％),doxycycline(88.75％),sulfisoxazole(77.17％),lincomycin(100％),and chloramphenicol(100％);the isolates were severely resistant.The isolates all carried virulence genes,with a total of five virulence genes detected,namely,EAST1(77.14％),eaeA(17.14％),stx2e(5.71％),LT(2.86％),and STb(2.86％),and the isolates also carried multi-re-sistance genes,with a total of five virulence genes detected,namely,bla TEM-1(65.71％),bla CTX-M(20.00％),tetA(82.86％),tetB(14.29％),aadA2(17.14％),aac(6')-Ib(14.29％),qnrS(17.14％),sul 1(40.00％),sul2(34.29％),and floR(60.00％);the phylogenetic grouping showed that the isolates had a high proportion of group B1 and group A;and all 35 isolates showed differ-ent pathogenicity after infection of mice.This study provides a reference for the selection of effec-tive therapeutic drugs and the development of prevention and control programs for swine-origin pathogenic E.coli in Hebei Province.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To establish a simultaneous detection approach for 34 emerging contaminants(ECs)in tap water by liquid chromatography-tandem mass spectrometry(HPLC-MS/MS).Human health risk assessment was performed according to the detection results from 43 tap water samples.Methods Tap water samples were concentrated and extracted by solid phase extraction,and then blown to near dry by nitrogen at 40℃.The sample extracts were dissolved in methanol-water solution(95:5,VN)to 0.5 mL for analyzing.Agilent Jet Stream Electrospray Ionization(AJS ESI)and the multiple reaction monitoring(MRM)mode were performed for MS to acquire the data of 34 ECs.A database including precursor ion,product ion and retention times was established accordingly.Results The average linear correlation coefficients(r)of 34 kinds of ECs was 0.995 9.The limits of detection were 0.01～0.60 ng/L and the recoveries were between 60.7％and 119.8％.The intra-group precisions were between 0.05％～9.89％and the intra-day precisions were between 0.20％～14.40％for the spiked samples.The method was applied to analyze 43 tap water samples and a total of 15 ECs were detected.According to the results,the detection rate of caffeine was the highest(84％),and the concentration range was ND～74.42 ng/L.Among all the ECs detected,1,2,3-benzotriazole had the highest concentration(ND～361.15 ng/L),where detection rate was 44％.Humans may be exposed to these ECs by drinking the tap water.The human health risk assessments of 12 kinds of ECs were carried out,however,the estimated risk was negligible(risk quotient＜0.01).Conclusion The method is simple,highly sensitive and selective,and could meet the detection needs of ECs at trace level in tap water.There was no human health risk posed for ECs identified in 43 tap water samples analyzed by this method.

ObjectiveTo investigate the levels of 25-hydroxyvitamin D ［25 （OH） D］ and its influencing factors in women during mid-pregnancy， and to provide an evidence for improving the vitamin D deficiency in pregnant women. MethodsA total of 220 pregnant women in the second trimester of pregnancy who underwent regular prenatal examinations at the Tianjin Binhai New Area Maternal and Child Health and Family Planning Service Center from February 2022 to February 2023 were selected as the research subjects. Basic clinical data of the pregnant women were collected. A 25 （OH） D level of ≥30 μg·L^-1 was considered sufficient， while a level of <30 μg·L^-1 was considered insufficient. Univariate analysis and multivariate logistic regression analysis were used to explore the factors influencing changes of 25 （OH） D levels in pregnant women. ResultsThe proportion of mid-pregnancy women with sufficient and insufficient 25 （OH） D levels were 15.9% （35/220） and 84.1% （185/220）， respectively. Univariate analysis showed that 25 （OH） D level in mid-pregnancy women was associated with maternal age， season of blood collection， weekly duration of outdoor activities， dietary preferences， calcium supplementation， multivitamin supplementation， and passive or active smoking， and all the differences were statistically significant （all P<0.01）. Multivariate logistic regression analysis revealed that women aged ≥35 years （OR=6.242， 95%CI： 2.501‒15.426， P<0.001）， with dietary preferences （OR=1.091， 95%CI： 1.034‒1.150， P<0.001）， and those who smoked or were exposed to passive smoking （OR=1.217， 95%CI： 1.084‒3.563， P<0.001）， or during winter （OR=2.196， 95%CI： 1.593‒3.024， P<0.001） were more likely to have 25 （OH） D deficiency. Conversely， women engaged in ≥10 hours of outdoor activities per week （OR=3.406， 95%CI： 1.818‒5.386， P<0.001）， supplemented with calcium （OR=1.811， 95%CI： 1.052‒3.116， P=0.032）， and supplemented with multivitamins （OR=3.662， 95%CI： 1.864‒5.386， P<0.001） had a relatively lower risk of 25 （OH） D deficiency. The difference was statistically significant. ConclusionThe incidence of 25 （OH） D deficiency is high in mid-pregnancy women and is primarily associated with maternal age， season of blood collection， weekly duration of outdoor activities， dietary preferences， and supplementation of multivitamins. It is necessary to conduct 25 （OH） D level screening and provide necessary medical interventions and corresponding educational programs for pregnant women.