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: 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.

ObjectiveBased on the clinical characteristics of chronic gouty arthritis （CGA） in both traditional Chinese and western medicine， this study aims to systematically evaluate the clinical concordance of existing CGA animal models， providing recommendations for establishing animal models that align with the pathological characteristics of CGA and the manifestations of traditional Chinese medicine syndromes. MethodsBy comprehensively retrieving Chinese and international databases such as China National Knowledge Infrastructure， Wanfang， VIP Chinese Science and Technology Periodical Database （VIP）， and PubMed， all relevant literature on CGA animal models was collected. Based on the guidelines， the diagnostic criteria of both traditional Chinese and western medicine were summarized and organized. The evaluation indicators for the CGA model were constructed with reference to existing evaluation modes， and the CGA animal models were analyzed to systematically evaluate the clinical concordance of existing models. ResultsThe current methods used to construct CGA animal models mainly include monosodium urate crystal induction， high-protein diet induction （poultry lack urate oxidase）， and high-fat diet combined with urate oxidase inhibitors and joint injection. Based on 11 pieces of included literature， the traditional Chinese and western medicine scoring data of each model were extracted， and the average scoring values of all models were ultimately calculated. The results show that the average clinical concordances of existing CGA animal models in both traditional Chinese and western medicine are 43.33% and 64.44%， respectively. Among them， the model with the highest clinical concordance rate is the one with a high-fat diet combined with potassium oxonate to induce hyperuricemia plus joint injection， achieving 83.33% clinical concordance in western medicine and 60% in traditional Chinese medicine. This model aligns well with the pathogenic characteristics and pathological changes of clinical CGA. ConclusionAlthough current CGA animal models can simulate some pathological characteristics of CGA， they struggle to comprehensively reflect the complex pathological processes of CGA and the characteristics of traditional Chinese medicine syndromes. Therefore， in the future， it is necessary to establish the CGA animal models that incorporate the clinical disease and syndrome characteristics of traditional Chinese and western medicine and formulate the uniform model evaluation criteria， providing more precise tools for CGA mechanism research and the development of traditional Chinese medicine.

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.

Objective: To analyze the epidemiological characteristics of infectious diseases among children and adolescents in Lanshan District, Linyi City, 2014-2023,so as to provide a scientific basis for prevention and control strategies in schools. Methods: The surveillance data were obtained from the China Information System for Disease Control and Prevention (CISDCP). Data on reported infectious disease cases and related demographic information among school children and kindergarteners in the study area from 2014 to 2023 were collected. Rate comparison was performed by using the χ 2 test. Trend analysis was conducted by using Joinpoint Regression Progrom, and the annual percent change (APC) and average annual percent change (AAPC) were calculated. Results: A total of 10 222 cases of infectious diseases among children and adolescents were reported from 2014 to 2023, with an average annual incidence rate of 273.32 per 100 000. The overall incidence trend remained stable from 2014 to 2023 (AAPC=12.33%, 95%CI=-2.63%-29.59%, P >0.05). Whereas a significant upward trend was observed from 2014 to 2019 (AAPC=37.08%, 95% CI =12.92%-66.41%, P <0.05), followed by a stable trend from 2019 to 2023 (AAPC=-0.31%, 95%CI=-39.70%-64.83%, P>0.05). Seasonal distribution exhibited dual peaks in summer and winter. The incidence rate in urban areas(707.29/100 000) was significantly higher than that in rural areas(257.50/100 000), and the incidence rate among kindergarteners (325.49/100 000) was higher than that among school children (257.07/100 000) (χ2=1 518.60, 116.36, both P<0.01). The reported infectious diseases among children and adolescents were predominantly respiratory and intestinal infectious diseases, with incidence rates of 156.13/100 000 and 108.51/100 000, respectively. Hand, foot, and mouth disease (HFMD) was the predominant infectious disease among kindergarteners, accounting for 47.51%. Influenza was the leading condition in the primary school group, accounting for 38.64%, and other infectious diarrheal diseases were most common in junior and senior high school group, accounting for 53.34%. The top six diseases by incidence rate were other infectious diarrheal diseases(65.22/100 000), influenza(63.24/100 000), varicella(46.34/100 000), HFMD(41.07/100 000), mumps(23.42/100 000), and scarlet fever(10.56/100 000). Among them, the incidence of other infectious diarrheal diseases rose significantly (AAPC=35.31%, 95% CI =14.32%-60.14%, P <0.01). The trend transitioned from a rapid escalation during 2014-2019 (APC=69.98%, 95% CI =17.58%-145.74%, P =0.01) to a stable phase during 2019-2023 (APC=1.74%, 95% CI =-15.61%- 22.66%, P >0.05). Conclusions In Lanshan, urban educational institutions should prioritize the prevention and control of infectious diseases, with particular emphasis on other infectious diarrheal diseases, influenza and varicella. Targeted interventions should be implemented in accordance with seasonal patterns and the specific characteristics of the school age populations.

A throat swab sample from a pediatric case in Tongzhou District, Beijing was identified as enterovirus; the patient was a 1-year-and-8-month-old male sporadic case. Whole-genome sequencing revealed a viral genome length of 7 436 bp. BLAST alignment confirmed the serotype as EV-D68. Phylogenetic analysis of the whole genome indicated that this strain belongs to the B3 clade, showing closer genetic proximity to the 2018 Shanghai strain MW697453 with 99.53% whole-genome nucleotide homology. Genetic and amino acid variation analysis demonstrated that the B3 subclade to which this strain belongs exhibits a nucleotide deletion at positions 718–726, differing from deletion sites observed in other B3 clade strains. A key neuropathogenic amino acid site, T650A, was found to have undergone mutation. Recombination analysis confirmed no cross-clade recombination events in this strain. This study conducted genetic characterization of the strain's evolutionary relationship with EV-D68 strains from different regions and years in China, providing data support for formulating prevention and control measures against EV-D68 infection.

Objective:This study investigated the expression level of phosphatidylserine-specific phospholipase A1(PLA1A)in colorectal can-cer(CRC)and analyzed its correlations with clinicopathological features,prognosis,and immune infiltration.Methods:The expression level of PLA1A in CRC was screened,and the influence of this expression level on patient prognosis was analyzed using bioinformatics methods.A cohort of 192 patients diagnosed with CRC at Shanxi Province Cancer Hospital from January to December 2020 were selected.The PLA1A ex-pression level in those with CRC was determined using immunohistochemistry(IHC)and real-time quantitative reverse transcription PCR(RT-qPCR).The relationship between PLA1A level and the clinicopathological features of the patients with CRC was analyzed using the chi-square test.The expression levels of immune cell markers CD4 and CD8 as well as immunosuppressive checkpoints PD-1,TIM-3,and CTLA-4 in CRC were detected via IHC,and their correlations with PLA1A level were analyzed using the chi-square test.Results:The results of bioinformatics analysis showed that the expression level of PLA1A in CRC tissue was higher than paracancerous tissue,which correlated with overall surviv-al(OS)(P<0.05).The IHC and RT-qPCR results showed that PLA1A expression level was significantly upregulated in CRC tissiue(P<0.05).High PLA1A level was closely associated with the TNM stage,degree of differentiation,and lymph node metastasis(P<0.05).The IHC results demonstrated that PLA1A positively correlated with the infiltrating CD8+T cell level(P<0.05).In addition,the elevated PLA1A levels upregu-lated the expressions of immunosuppressive checkpoints PD-1,TIM-3,and CTLA-4(P<0.05).Conclusions:PLA1A is highly expressed in CRC,which is closely related to immune infiltrating cells and immunosuppressive checkpoints,suggesting that PLA1A plays an important role in immune infiltration in CRC,a finding that provides guidance in the treatment of CRC.

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.

Objective:To propose a novel radiotherapy marking method-the"#"-character method,which aimed at improving the accuracy and repeatability of positioning during radiotherapy.Methods:A specially"cross-shaped"stamp was designed by this study,which consisted of a handheld square base with a"cross-shaped"protrusion.Using this stamp,the extreme positions of end-expiration and end-inspiration were marked respectively at the laser-guided regions on the directly above and bilateral sides of the patient's body,and each position was printed a"+"character.Finally,a"#-shaped"signal was formed,which represented the full range of respiratory motion of patients.The study included two parts:surface displacement caused by respiration was simulated through a three-dimensional(3D)motion platform,which was used to conduct a phantom experiment for anthropomorphic dummy,A randomized controlled study involving 40 patients,who were treated between January and June 2024 at the Department of Radiotherapy,Cancer Hospital,Chinese Academy of Medical Sciences,were conducted.The cohort included 20 patients with breast tumor(Positioning the outer contour by exposing the chest)and 20 patients with thoracic tumor(fixed position of using thermoplastic film).These patients were divided into two groups for comparison,which received respectively the"#-shaped"method and the conventional"+-shaped"method.The cone-beam computed tomography(CBCT)images before treatment were used to compare the influences of the two kinds of marking methods on the positioning errors of patients with breast tumor and patients with thoracic tumor.Then,the statistical analysis was used to assess precision and accuracy of positioning.Results:The result of phantom experiment indicated that the positioning error of the"#-shaped"method was significantly better than that of the"+-shaped"method under various parameters of respiratory movement.Under three kinds of different respiratory cycles(3,4,and 5 seconds)and amplitudes(8,12,and 15 mm),the positioning errors of the"#-shaped"method were respectively(0.15±0.04)cm,(0.19±0.05)cm and(0.35±0.14)cm,while the"+-shaped"method were respectively(0.42±0.16)cm,(0.64±0.28)cm and(0.88±0.37)cm,and the differences were statistically significant(t=8.347,3.416,2.901,P<0.05).The results of actual patients indicated the positioning error[(0.97±0.32)cm]of the"#-shaped"method was significantly lower than[(1.62±0.47)cm]of the"+-shaped"method for patients with breast tumor(Positioning the outer contour by exposing the chest),and the difference was significant(t=3.615,P<0.05).On the other hand,the positioning error[(0.69±0.24)cm]of the"#-shaped"method was significantly lower than[(0.97±0.39)cm]of the"+-shaped"method for patients with thoracic tumor(fixed position of using thermoplastic film),and the difference also was significant(t=1.934,P<0.05).Conclusion:Compared to the conventional"+-shaped"method,the"#-shaped"method appears higher accuracy and repeatability during the positioning process of radiotherapy,which especially is suitable to the treatment for breast tumor and thoracic tumor that need accurately control the influences of respiratory motion.