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: 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.

Cardiovascular diseases （CVD）， particularly atherosclerosis， represent a major global health burden. Recent studies have revealed that innate immune cells such as monocytes and macrophages can develop immune memory after an initial stimulus， a phenomenon termed “trained immunity”. Growing evidence indicates that trained immunity serves as an underlying mechanism of chronic inflammation in atherosclerotic cardiovascular diseases. This review focuses on outlining the key effector cells involved in trained immunity and their mechanisms of formation， including processes such as metabolic reprogramming and epigenetic modifications， which collectively lead to a heightened immune response upon secondary stimulation. Furthermore， this review systematically summarizes the role of trained immunity in the initiation and progression of atherosclerosis， and elaborates on various therapeutic strategies targeting trained immunity along with their application prospects.

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.

AIM: To explore and compare the effect of corneal epithelial thickening on predictability of 7 common intraocular lens(IOL)calculation formulas: the Barrett Universal II, Haigis, Holladay 2, Kane, EVO, Hill-RBF and SRK/T.METHODS: Patients undergoing phacoemulsification and IOL implantation surgery from July 2023 to September 2023 were included. Before the surgery, biometry data was obtained by IOLMaster 700 and epithelial thickness was determined using optical coherence tomography. A monofocal IOL(Hoya iSert 250)was inserted into the capsular bag. The postoperative refraction was measured 3 mo after the surgery. The median epithelial thickness of 57.0 μm was set as a grouping criterion: ≥57.0 μm(thick group)and Symbol|@@57.0 μm(thin group). The percentage of eyes within prediction errors of ±0.25 D, ±0.50 D, ±0.75 D and ±1.00 D, mean absolute error(MAE), and median absolute error(MedAE)were reviewed.RESULTS: Fifty-nine eyes of 59 patients(34 females, 25 males, mean age 68.85±8.71 y)were included in the study. The thin group included 30 eyes and the thick group included 29 eyes. Among all 59 eyes, Barrett Universal II showed the highest percentage of prediction error within ±0.50 D(83%), followed by Hill RBF(81%). Kane(71%)and EVO(73%)exhibited the lowest percentages. When calculating the percentage of eyes with absolute error within ±0.25 D using seven IOL calculation formulas, most formulas showed significant declined accuracy in the thick group compared with the thin group(U=6.00, PSymbol|@@0.05). Hill RBF showed better accuracy in eyes with thickened epithelium, with higher percentage of prediction error within ±0.25 D(52%)and ±0.50 D(83%).CONCLUSION: The accuracy of IOL calculation is associated with corneal epithelium thickening. Hill-RBF has better validity in eyes with thickened corneal epithelium.

Objective: To investigate the occupational health literacy (OHL) level and its influencing factors of key populations in Jinhua City, Zhejiang Province, so as to provide a basis for promoting occupational health. Methods: The front-line workers of 7 secondary industry enterprises (institutions) and 15 tertiary industry enterprises (institutions) in Jinhua City were selected from April to October 2023 using a stratified random cluster sampling method. Date of gender, age, length of service, and OHL were collected using the National Occupational Health Literacy Monitoring Survey for Key Population Personal Questionnaire. Factors affecting OHL level among key populations were identified using multivariable logistic regression model. Results: A total of 3 305 people were investigated, including 1 750 males (52.95%) and 1 555 females (47.05%). The median age and the length of service were 37 (interquartile range, 17) and (interquartile range, 9) years, respectively. The level of OHL was 45.63%. Multivariable logistic regression analysis identified gender (female, OR=1.675, 95%CI: 1.428-1.964), educational level (junior high school, OR=1.499, 95%CI: 1.089-2.063; high school, OR=1.905, 95%CI: 1.361-2.667; junior college, OR=4.065, 95%CI: 2.858-5.782; bachelor degree and above, OR=5.087, 95%CI: 3.597-7.194), personal monthly income (3 000-< 5 000 yuan, OR=1.373, 95%CI: 1.035-1.821; 5 000-<7 000 yuan, OR=1.653, 95%CI: 1.230-2.220; ≥7 000 yuan, OR=1.798, 95%CI: 1.322-2.447) and length of service (2-<6 years, OR=1.265, 95%CI: 1.032-1.551; 6-<11 years, OR=1.517, 95%CI: 1.184-1.943; ≥11 years, OR=1.337, 95%CI: 1.040-1.719) as factors affecting OHL level among key populations. Conclusions The OHL level of the key populations in Jinhua City is related to gender, age, education level, personal monthly income, and length of service. It is necessary to strengthen the occupational health education and health promotion of the key populations.

OBJECTIVE To systematically investigate the changes of volatile components in Euphorbia wallichii after milk and wine processing， and preliminarily elucidate the material basis for reducing toxicity. METHODS Using headspace gas chromatography-mass spectrometry technology， the volatile components in raw E. wallichii， milk-processed E. wallichii， and wine- processed E. wallichii were isolated and identified， and the relative percentage content of each component was calculated by the peak area normalization method. Combining chemometric methods such as principal component analysis and orthogonal partial least- squares discriminant analysis， changes in volatile components in samples after milk and wine processing were compared. Differential components were screened. RESULTS A total of 66 volatile components were identified from the three samples， with the types of compounds primarily comprising alkanes， olefins， heterocycles and esters， among others. A total of 39， 24 and 36 volatile components were identified from raw E. wallichii， milk-processed E. wallichii， and wine-processed E. wallichii， respectively， with 10 components common to all three preparations. Compared with raw E. wallichii， the relative percentage of other components in milk-processed E. wallichii decreased， except for alkanes and esters. The relative percentage of alkanes， olefins， aldehydes and esters in wine-processed E. wallichii increased， but the contents of heterocyclic compounds， ketones， ethers and alcohols decreased. The results of chemometric analysis showed that the volatile components of raw and processed products were significantly different. A total of 5 kinds of differential components in milk-processed products and 3 kinds of differential components in wine-processed products were screened out. Among them， the relative percentage of potential toxic components such as linalool， octanal and 3-pentanone decreased significantly after processing（P＜0.05）. CONCLUSIONS Milk and wine processing may exert a toxicity-reducing effect by reducing the contents of toxic components such as linalool， octanal and 3-pentanonein E. wallichii.