Objective: To analyze the school tuberculosis (TB) outbreaks and preventive treatment in Hefei from 2022 to 2024, so as to provide reference for TB prevention and control in schools. Methods: Data were collected on all school based TB outbreaks occurring during 2022-2024 in Hefei, defined as ≥2 epidemiologically linked TB cases within the same school during a single semester. Statistical analyses were performed using the Chi square test. Results: Close contacts exhibited significantly higher TB incidence (2.88%) and latent mycobacterium tuberculosis infection (LTBI) rates (13.80%) in the school TB outbreaks, compared to non close contacts (0.12% and 2.63%, respectively). Among close contacts, secondary school students showed lower TB incidence (0.48%) and LTBI prevalence (3.42%) than both primary school or younger children (0.68%, 6.95%) and college students ( 0.78% , 6.50%), with statistically significant differences ( χ 2=360.91, 6.37; 791.71, 102.03, all P <0.05). The proportion of LTBI individuals recommended for preventive therapy was higher in primary school or younger groups (98.59%) than in secondary (95.25%) or college students (86.34%) ( χ 2=25.86, P <0.01). However, among those recommended, close contacts had higher uptake (85.82%) and completion rates (87.25%) of preventive therapy than non close contacts (69.63% and 70.57%); similarly, secondary school students demonstrated higher uptake (91.21%) and completion rates (86.45%) compared to primary school or younger (88.57%, 83.87%) and college students (57.28%, 64.08%) ( χ 2=30.52, 26.72; 125.17, 38.84, all P <0.01). Subsequent TB incidence among LTBI close contacts (13.30%) and among those who did not complete preventive therapy (22.73%) were significantly higher than among non close contacts (2.80%, 2.41%), respectively ( χ 2=32.19, 13.87, both P <0.05). Conclusions In school TB outbreaks, close contacts face higher LTBI prevalence and subsequent TB risk than non close contacts. College students show notably low adherence to preventive therapy. It is necessary to take targeted measures to improve the compliance of preventive measures among students.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

The detection rate of carbapenem-resistant Enterobacter cloacae(CRECL)in clinical practice has risen steadily in recent years,and it has become one of the main pathogens of hospital-acquired infections.CRECL resistance is primarily driven by carbapenemase-encoding genes(CEG),which can spread through multiple mechanisms under the selective pressure of antibiotics and other environmental factors.This gene transfer significantly hampers antimicrobial treatment and infection control efforts.This review outlines the current understanding of CEG distribution in CRECL,key transmission pathways,and their public health im-plications.The findings aim to support the development of effective strategies for the prevention and manage-ment of CRECL infections.

Drug-induced liver injury(DILI)is a common adverse drug reaction in clinical practice,which can lead to acute liver failure and even death in severe cases.In recent years,with the continuous introduction of new drugs and the expansion of their usage,the incidence and mortality rates of DILI have shown an upward trend,posing significant challenges to public health and clinical treatment.Macrophages,as a crucial component of the innate immune system,exhibit high plasticity and heterogeneity.They can polarize into pro-inflammatory M1 type or anti-inflammatory M2 type in response to microenvironmental signals.Research has demonstrated that macrophage polarization plays a central regulatory role in the occurrence and progression of DILI by influencing various processes such as inflammatory responses,cell apoptosis,and tissue repair.This review focuses on elucidating the regulatory mechanisms and roles of macrophage polarization in DILI,providing a theoretical framework for developing precise immunotherapeutic strategies.

Objective To investigate the predictive value of serum circular RNA(circRNA)CDYL and circRNA ACAP2 expression levels for major adverse cardiovascular events(MACE)in pa-tients with acute myocardial infarction(AMI).Methods A total of 98 AMI patients were enrolled into the observation group,and divided into MACE group(n=45)and no-MACE group(n=53)based on whether they experienced MACE.Another 98 healthy individuals who underwent physical ex-amination during the same period were selected as the control group.The expression levels of serum cir-cRNA CDYL and circRNA ACAP2 were compared among groups.Multivariate logistic regression analysis was used to identify factors influencing the occurrence of MACE in AMI patients.Receiver operating characteristic(ROC)curve analysis was conducted to evaluate the predictive value of serum circRNA CDYL and circRNA ACAP2 levels for MACE in AMI patients.Results The serum circRNA CDYL level in the observation group was significantly lower than that in the control group,while the circRNA ACAP2 level was significantly higher(P＜0.05).The circRNA CDYL level in the MACE group was significantly lower,and the circRNA ACAP2 level was significantly higher than that in the no-MACE group(P＜0.05).Heart rate and circRNA ACAP2 were identified as independent risk factors for MACE in AMI patients,whereas circRNA CDYL served as independent protective factor(P＜0.05).The area under the ROC curve(AUC)for predicting MACE in AMI patients using se-rum circRNA CDYL,circRNA ACAP2 and their combination were 0.814,0.821 and 0.921,re-spectively.The sensitivity and specificity of combined prediction using serum circRNA CDYL and circRNA ACAP2 were 91.11％and 79.25％,respectively.The combined prediction efficacy of serum circRNA CDYL and circRNA ACAP2 was superior to their individual prediction(Zcombined-circRNA CDYL=1.975,Zcombined-circRNAACAP2=2.064,P=0.048,0.039).Conclusion Serum circRNA CDYL lev-el is down-regulated and circRNA ACAP2 level is up-regulated in AMI patients,and circRNA ACAP2 is an independent risk factor for MACE,while circRNA CDYL is a independent protective factor.The combined value of circRNA CDYL and circRNA ACAP2 in predicting the occurrence of MACE is higher.

Objective To construct and validate a risk prediction model for in-hospital death af-ter successful resuscitation in patients with cardiac arrest.Methods A retrospective study was con-ducted on 295 patients with cardiac arrest who successfully restored spontaneous circulation after car-diopulmonary resuscitation and were further treated in hospital.The patients were divided into training and validation sets using K-fold cross-validation and then grouped and compared based on whether in-hospital death occurred.A binary Logistic regression analysis was used to screen risk prediction fac-tors,and a nomogram prediction model was constructed.The model performance was evaluated and validated in the training and validation sets,respectively.Results The results of the multivariate Logistic regression analysis showed that hospitalization duration(OR=1.180;95％CI,1.080 to 1.280;P＜0.001),norepinephrine dose(OR=0.980;95％CI,0.970 to 0.990;P=0.002),ini-tial respiratory rate after resuscitation(OR=1.090;95％CI,1.030 to 1.150;P=0.004),and sinus rhythm recovery after resuscitation(OR=4.280;95％CI,1.670 to 10.980;P=0.003)were inde-pendent influencing factors for in-hospital death.A nomogram model was constructed based on these in-dependent influencing factors,and it was verified that the model had good discrimination,calibration,applicability,and rationality.Conclusion The influencing factors for in-hospital death after successful resuscitation in patients with cardiac arrest include hospitalization duration,norepinephrine dose,initial respiratory rate after resuscitation,and sinus rhythm recovery after resuscitation.The nomo-gram model constructed based on these factors can provide a reference for clinical decision-making.

Objective:To investigate the outcomes of natural cycle (NC) and modified natural cycle (MNC) assisted reproductive technology (ART) in patients with diminished ovarian reserve (DOR), and to provide a scientific basis for individualized treatment strategies for DOR patients.Methods:A retrospective cohort analysis was performed on the clinical data of DOR patients who underwent ART at the Center for Reproductive Medicine of the Department of Obstetrics and Gynecology, Peking University Third Hospital from January 1, 2015 to December 31, 2023. Patients were divided into the NC group ( n=801) and the MNC group ( n=385) based on their treatment protocol. The primary outcomes were cycle cancellation rate and oocyte retrieval rate. Secondary outcomes included clinical pregnancy rate and live birth rate per fresh embryo transfer cycle and frozen-thawed embryo transfer cycle, cumulative pregnancy rate and cumulative live birth rate per started cycle and per transfer cycle, as well as laboratory parameters such as the number of retrieved oocytes, the number of two pronuclei (2PN) fertilized oocytes, the number of transferable embryos, and transferable embryo formation rate. Further, multivariate logistic regression was used to analyze the impact of the treatment protocol on pregnancy and live birth outcomes. Results:There were no statistically significant differences between the NC and MNC groups in terms of general characteristics such as age, body mass index, and baseline hormone levels (all P>0.05). The cycle cancellation rate was significantly higher in the NC group [19.10% (153/801)] than in the MNC group [10.65% (41/385), P<0.001], and the oocyte retrieval rate was significantly lower in the NC group [66.31% (431/650)] than in the MNC group [74.86% (259/346), P=0.005]. The number of retrieved oocytes [1 (0,1)], the number of 2PN fertilized oocytes [1 (0,1)], and the number of transferable embryos [0 (0, 1)] were also significantly lower in the NC group than in the MNC group [1 (1, 2), P<0.001; 1 (1, 1), P<0.001; 0 (0, 1), P<0.001]. However, there were no statistically significant differences in 2PN fertilization rate and transferable embryo formation rate between the NC and MNC groups (all P>0.05). In both fresh embryo transfer cycles and frozen-thawed embryo transfer cycles, there were no statistically significant differences in clinical pregnancy rate and live birth rate between the NC and MNC groups (all P>0.05). The cumulative pregnancy rate per started cycle and transfer cycle, the cumulative live birth rate per started cycle and per transfer cycle were also not significantly different between the NC and MNC groups (all P>0.05). Multivariate logistic analysis showed no significant association between NC and clinical pregnancy or live birth compared with MNC. Conclusion:While MNC to some extent reduced the cycle cancellation rate and improved oocyte retrieval rates compared with NC, it did not ultimately improve pregnancy outcomes in DOR patients.

Objective To understand the current situation and influencing factors of salary satisfaction of staff in tertiary public hospitals,and provide suggestions for improving the reform of salary system.Methods A questionnaire survey was conducted on 816 employees from 7 tertiary public hospitals in Hunan Province,and the basic situation,salary level,salary promotion,salary system and salary distribution were studied.Descriptive analysis and binary logistic regression model were used to analyze the status quo and influencing factors of employee salary satisfaction.Results The overall satisfaction score of the 816 survey respondents'salary was 71.56 points,at a relatively high level.There was a large difference in satisfaction levels among different regions.Salary level and salary increase satisfaction were 69.19 and 70.90 points respectively,and satisfaction was low.Region,affiliation with a university,cadre status,and title were the main factors affecting salary satisfaction.Conclusion The salary satisfaction of staff in tertiary public hospitals was generally higher in Hunan Province,and the reform had shown positive results.The satisfaction with salary increases and salary levels was relatively low,and it was suggested that the government reasonably determine the salary level of public hospitals and establish a scientific and reasonable salary increase mechanism.It will improve supporting reforms and promote equal pay for equal work.