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.

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.

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 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.

Objective:To investigate the pregnancy outcomes and cumulative live birth rate (CLBR) after in vitro fertilization/intracytoplasmic sperm injection and embryo transfer (IVF/ICSI-ET) in patients with high basal follicle-stimulating hormone (bFSH) levels. Methods:This retrospective cohort study included clinical data from patients who underwent IVF/ICSI-ET treatment at the Reproductive Medical Center of Peking University Third Hospital from January 2018 to December 2022. Patients were divided into three groups based on the highest bFSH level during all cycles of treatment: group A (15 U/L≤bFSH<25 U/L), group B (25 U/L≤bFSH<40 U/L), and group C (bFSH≥40 U/L). After propensity score matching (PSM) based on the female body mass index, the baseline data, embryology laboratory outcomes, and assisted reproductive outcomes such as clinical pregnancy rate among the three groups of patients were compared. Binary logistic regression analysis was used to explore the impact of various factors on live birth, and the trend of CLBR across multiple cycles was also studied.Results:After PSM, 340 cycles were included in group A, 340 cycles were included in group B, 127 cycles were included in group C. There were statistically significant differences among the three groups in antral follicle count, bFSH, basal progesterone, basal luteinizing hormone, and anti-Müllerian hormone levels ( P=0.004, P<0.001, P<0.001, P<0.001, P<0.001). In the analysis of controlled ovarian stimulation (COS) protocols, groups A and B mainly used conventional COS protocol, while group C primarily used mild stimulation protocol. The duration and dosage of gonadotropin used were the highest in group A [10 (7, 12) d, 2 728 (1 650, 3 725) U], with statistically significant differences among the three groups (all P<0.001). On the day of human chorionic gonadotropin injection, there were statistically significant differences in estradiol and progesterone levels among the three groups ( P=0.022 and P=0.048, respectively). The cancellation rate of cycles did not differ significantly among the three groups ( P>0.05), while the number of oocytes retrieved ( P<0.001) and the rate of cycles with no transferable embryos ( P=0.034) showed statistically significant differences. The type of embryos transferred in all three groups was mainly cleavage-stage embryos, and there were statistically significant differences in the rate of two pronuclei and high-quality embryos among the groups ( P=0.003 and P=0.006, respectively). The rate of high-quality embryos decreased with increasing bFSH levels, and comparisons between group A and group B, as well as group A and group C, showed statistically significant differences (all P<0.016 7). The biochemical pregnancy rate and the clinical pregnancy rate in fresh cycles differed significantly among the three groups ( P=0.025 and P=0.010, respectively), while the live birth rate per initiated cycle showed marginal significance ( P=0.058). However, the miscarriage rate and the live birth rate per transfer cycle did not differ significantly among the groups (all P>0.05). Binary logistic regression analysis revealed that bFSH ( OR=0.955, 95% CI: 0.912-1.000, P=0.050) and the number of oocytes retrieved ( OR=1.104, 95% CI: 1.009-1.207, P=0.031) were independent predictors of live birth. Analysis of CLBR curves across multiple oocyte retrieval cycles showed that CLBR gradually increased with the number of oocyte retrievals and stabilized at 14.32% after the fifth retrieval. Conclusion:High bFSH levels reduce the live birth rate per initiated cycle but do not affect the live birth rate per transfer cycle. Increasing age and a low number of oocytes retrieved can both decrease the live birth rate. Multiple oocyte retrieval and transfer cycles can improve CLBR in patients with high bFSH level to some extent, but it tends to stabilize after the fifth cycle.

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 explore the potential mechanism of Cigu Xiaozhi Prescription(CGXP)in the treatment of non-alco-holic steatohepatitis(NASH)liver fibrosis.METHODS A NASH mouse model was established.The degree of liver enlargement was evaluated by calculating the liver index.Hematoxylin-eosin(HE)and Masson staining were used to observe the degree of liver fibro-sis.In addition,immunohistochemistry was employed to detect the expression of liver fibrosis-related proteins,including α-SMA,Collagen 1,MMP2,and MMP9.Western blot and qPCR techniques were used to detect the expression levels of HIF-1α,E-cadher-in,N-cadherin,Shh,Smo,Gli1,and Gli2 in the mouse liver.The alkaline hydrolysis method was used to measure the content of liv-er hydroxyproline.RESULTS CGXP could effectively reduce the liver index(P<0.001),alleviate liver enlargement and inflamma-tion,and significantly improve the pathological damage of liver tissue in mice with liver fibrosis.CGXP significantly decreased the ex-pression levels of liver fibrosis-related proteins α-SMA,Collagen 1,MMP2 and MMP9(P<0.01,P<0.000 1);reduced the levels of HIF-1α,E-cadherin,N-cadherin,Shh,Smo,Gli1,and Gli2,and the therapeutic effect of high-dose CGXP was particularly significant(P<0.05,P<0.01,P<0.001,P<0.000 1).CONCLUSION CGXP can relieve NASH liver fibrosis in mice by reducing the liver index,alleviating inflammation,and improving tissue pathological damage.The mechanism may be related to the inhibition of the Hedgehog signaling pathway,which alters the activation and proliferation of hepatic stellate cells and reduces the synthesis and dep-osition of extracellular matrix.