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.

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.

BACKGROUND:Degenerative scoliosis is defined as a condition that occurs in adulthood with a coronal cobb angle of the spine>10° accompanied by sagittal deformity and rotational subluxation,which often produces symptoms of spinal cord and nerve compression,such as lumbar pain,lower limb pain,numbness,weakness,and neurogenic claudication.The finite element method is a mechanical analysis technique for computer modelling,which can be used for spinal mechanics research by building digital models that can realistically restore the human spine model and design modifications. OBJECTIVE:To review the application of finite element method in the etiology and treatment of degenerative scoliosis. METHODS:The literature databases CNKI,PubMed,and Web of Science were searched for articles on the application of finite element method in degenerative scoliosis published before October 2023.Search terms were"finite element analysis,biomechanics,stress analysis,degenerative scoliosis,adult spinal deformity"in Chinese and English.Fifty-four papers were finally included. RESULTS AND CONCLUSION:(1)The biomechanical findings from the degenerative scoliosis model constructed using the finite element method were identical to those from the in vivo experimental studies,which proves that the finite element method has a high practical value in degenerative scoliosis.(2)The study of the etiology and treatment of degenerative scoliosis by the finite element method is conducive to the prevention of the occurrence of the scoliosis,slowing down the progress of the scoliosis,the development of a more appropriate treatment plan,the reduction of complications,and the promotion of the patients'surgical operation.(3)The finite element method has gradually evolved from a single bony structure to the inclusion of soft tissues such as muscle ligaments,and the small sample content is increasingly unable to meet the research needs.(4)The finite element method has much room for exploration in degenerative scoliosis.

Objective:To investigate the iodine nutrition level and the prevalence of thyroid nodules in pregnant women in Hubei Province, and to provide a basis for prevention and treatment of iodine deficiency disorders.Methods:According to the requirements of the National Iodine Deficiency Disorders Monitoring Program (2016 Edition), a cross-sectional survey of iodine nutrition status of pregnant women ( n = 321) was conducted from July to October 2020 in two mountainous counties (Tongcheng County and Xingshan County) and two plain counties (Liangzihu District and Xinzhou District) in Hubei Province. Among them, there were 43, 114, and 164 pregnant women in the early, middle, and late stages of pregnancy, respectively. Edible salt samples and once random urine samples were collected to detect salt iodine and urinary iodine, and thyroid ultrasound was performed to calculate the detection rate of thyroid nodules. Results:The coverage rate of iodized salt, qualified rate of iodized salt, and consumption rate of qualified iodized salt in Hubei Province were 99.69% (320/321), 95.94% (307/320) and 95.64% (307/321), respectively. The median urinary iodine level for pregnant women was 164.80 μg/L. Among them, the median urinary iodine levels in Liangzihu District, Tongcheng County, Xinzhou District, and Xingshan County were 175.90, 178.25, 155.80 and 143.00 μg/L, respectively. There was a statistically significant difference in urinary iodine levels among different regions ( H = 8.51, P = 0.037). The median urinary iodine levels of pregnant women in the early, middle, and late stages of pregnancy were 187.20, 144.45, and 172.05 μg/L, respectively. There was no statistically significant difference in urinary iodine levels among pregnant women in different stages of pregnancy ( H = 2.94, P = 0.230). Urinary iodine < 150, 150 - < 250, 250 - < 500, ≥500 μg/L accounted for 45.48% (146/321), 33.33% (107/321), 19.63% (63/321), 1.56% (5/321), respectively. The detection rate of thyroid nodules was 16.82% (54/321), and the goiter rate was 0.93% (3/321). Conclusions:In 2020, Hubei Province is in an appropriate state of iodine, and there are still a considerable proportion of pregnant women in a state of iodine deficiency. The detection rate of thyroid nodules is relatively low. It is necessary to continuously monitor the iodine nutrition of pregnant women, strengthen health promotion on the hazards of iodine deficiency during pregnancy, and minimize maternal and infant health damage caused by iodine deficiency.

Objective:To study the relationship between urinary arsenic methylation metabolism patterns and skin keratinization and pigmentation abnormalities in population exposed to arsenic through drinking water.Methods:Using a cross-sectional study method, a survey on endemic arsenic poisoning was conducted among permanent residents of drinking water endemic arsenic poisoning areas in Bayannur City, Inner Mongolia Autonomous Region in 2004 (before water improvement). In 2017 (after water improvement), 71 arsenic exposed individuals were followed up as survey subjects. According to the "Diagnosis of Endemic Arsenism" (WS/T 211-2015), the clinical grading of skin injuries (skin keratinization, pigmentation abnormalities) in the survey subjects was evaluated. Urine samples were collected for detection of arsenic methylation metabolite levels by high-performance liquid chromatography inductively coupled plasma mass spectrometry and calibrated with urinary creatinine. The changes and amplitudes of urinary arsenic methylation indicators before and after water improvement were calculated and analyzed according to the outcome of skin keratinization and pigmentation abnormalities which were divided into reduced, unchanged, and added groups.Results:(1) The changes in urinary total arsenic (TAs), inorganic arsenic (iAs), monomethyl arsenic (MMA), and dimethyl arsenic (DMA) levels in different outcome groups of skin keratinization were compared, and the differences were statistically significant ( H = 9.08, 8.77, 9.28, 8.57, P < 0.05). The changes in urinary TAs, iAs, MMA, DMA levels, iAs percentage (iAs%), DMA percentage (DMA%), and primary methylation index (PMI) in different outcome groups of skin pigmentation abnormalities were compared, and the differences were statistically significant ( H = 8.04, 10.67, 8.29, 9.14, 6.30, 9.10, 7.20, P < 0.05). (2) The comparison of amplitudes in urinary TAs, iAs, MMA, and DMA levels in different outcome groups of skin keratinization showed statistically significant differences ( H = 6.92, 7.34, 6.66, 6.16, P < 0.05). The amplitudes in urinary iAs level, iAs%, DMA%, and PMI in different outcome groups of skin pigmentation abnormalities were compared, and the differences were statistically significant ( H = 7.94, 7.61, 9.95, 7.22, P < 0.05). Conclusion:The changes pattern of urinary TAs, iAs, MMA, DMA, iAs%, DMA%, and PMI in population exposed to arsenic through drinking water is related to the transformation of skin keratinization and pigmentation abnormalities.

Objective:To study the relationship between the levels of multiple elements in urine and the risk of arsenic poisoning in populations exposed to drinking water arsenic in Inner Mongolia Autonomous Region (Inner Mongolia).Methods:From April 2023 to January 2024, a case-control study method was used to select 128 individuals with a residence time of ≥10 years in drinking water arsenic exposed areas in Inner Mongolia as study subjects. Eighty-one individuals diagnosed with arsenic poisoning were selected as the case group, and 47 healthy individuals were selected as the control group for urine sample collection and questionnaire survey. Inductively coupled plasma mass spectrometry was employed to determine the levels of 10 elements (chromium, manganese, cobalt, nickel, copper, zinc, arsenic, molybdenum, cadmium and lead) in urine. The levels of each element in urine were divided into four groups ( Q1, Q2, Q3, and Q4 groups) based on quartiles. The associations between the levels of various elements in urine and the risk of arsenic poisoning were studied using binary logistic regression model and restricted cubic spline (RCS). Results:The age of the control group and the case group [ M ( Q1, Q3)] were 61 (53, 69) and 61 (56, 67) years old, respectively. There were 19 and 43 males, and 28 and 38 females, respectively. There was no statistically significant differences in age and and gender composition between the two groups ( Z = - 0.39, P = 0.700; χ 2 = 1.91, P = 0.167). The levels of urinary copper and cadmium of the case group were higher than those of the control group, and the differences were statistically significant ( Z = - 2.66, - 2.16, P < 0.05). The results of univariate logistic regression analysis showed that urinary copper was an influencing factor for arsenic poisoning ( P = 0.017). The results of multivariate logistic regression analysis revealed that after adjusting for covariates, urinary copper and arsenic were independent influencing factors of arsenic poisoning ( P < 0.05). Taking Q1 group as a reference, urinary copper in Q3 group [ OR (95% CI) = 8.23 (1.81, 37.39), P = 0.006] increased the risk of arsenic poisoning, while urinary arsenic in Q2, Q3, and Q4 groups [ OR (95% CI) = 0.24 (0.06, 0.92), 0.12 (0.03, 0.53), 0.15 (0.04, 0.63), P < 0.05] decreased the risk of arsenic poisoning. After adjusting for covariates, RCS did not show a dose-response relationship between urinary copper, urinary arsenic, and arsenic poisoning ( P > 0.05). Conclusion:Urinary arsenic and copper are associated with the risk of arsenic poisoning in the drinking water arsenic exposed areas of Inner Mongolia, copper exposure may contribute significantly to arsenic poisoning.

Objective:To investigate the completion rate of tumor evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients in the national multicenter real-world database.Methods:The prospective real-world study was conducted. The clinicopathological data of 1 074 patients who underwent surgical treatment for mid and low rectal cancer in 47 national medical institutions, including Peking Union Medical College Hospital et al, from May 12,2023 to May 11,2024 were collected. Observation indicators: (1) clinical characteristics of patients with mid and low rectal cancer; (2) initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer; (3) initial imaging evaluation of patients with mid and low rectal cancer; (4) imaging evaluation after neoadjuvant therapy for patients with mid and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M( Q1, Q3). Count data were described as absoluter numbers and/or percentages. Results:(1) Clinical characteristics of patients with mid and low rectal cancer. Of the 1 074 patients, there were 713 males and 361 females, aged 63(56,70)years. The body mass index of 1 074 patients was 24(21,26)kg/m 2.For American Society of Anesthesiologists classification, there were 147 cases of stage Ⅰ, 641 cases of stage Ⅱ, 157 cases of stage Ⅲ, 2 cases of stage Ⅳ, and there were 127 cases missing data. (2) Initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer. Of the 1 074 patients, there were 787 cases (73.28%) undergoing complete colonoscopy, and there were only 197 cases (18.34%) undergoing immunohistochemical evaluation of all four mismatch repair proteins. (3) Initial imaging evaluation of patients with mid and low rectal cancer. Of the 1 074 patients, there were 842(78.40%) patients completing magnetic resonance imaging (MRI) or ultrasound evaluation, and there were 914(85.10%) patients completing chest, abdomen, and pelvis enhanced computed tomography (CT) evaluation. In the 149 patients completing rectal ultrasound evaluation, there were 122 cases (81.88%) comple-ting T staging evaluation, and there were 81 cases (54.36%) completing N staging evaluation. In the 808 patients completing rectal MRI evaluation, there were 708 cases (87.62%) completing T staging evaluation, and there were 590 cases (73.02%) completing N staging evaluation. (4) Imaging evalua-tion after neoadjuvant therapy for patients with mid and low rectal cancer. Of the 388 patients with neoadjuvant therapy, there were 332 patients (85.57%) completing MRI or ultrasound evaluation, and there were 327 patients (84.28%) completing chest, abdomen, and pelvis enhanced CT evalua-tion. In the 70 patients completing rectal ultrasound evaluation, there were 65 cases (92.86%) com-pleting T staging evaluation, and there were 49 cases (70.00%) completing N staging evaluation. In the 327 patients completing rectal MRI evaluation, there were 246 cases (75.23%) completing T staging, and there were 228 cases (69.72%) completing N staging evaluation. Conclusion:The com-pletion rate of tumor imaging evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients on a national scale is relatively good.

Aim To investigate the protective effect of liraglutide(LIRA)on acetaminophen(APAP)-in-duced hepatotoxicity at the in vivo level and to reveal the underlying mechanism.Methods Forty SPF grade male C57BL/6J mice were randomly divided into the Control,LIRA(200 μg·kg-1),APAP(500 mg·kg-1),LIRA+APAP,LIRA+APAP+3-methylade-nine(3-MA,30 mg·kg-1)groups,with eight mice in each group.The mice were administered for three con-secutive days,and the materials were taken after 24 h.The general condition and body weight of mice in each group were recorded,and liver morphology was ob-served.Serum ALT and AST levels,as well as SOD ac-tivity,MDA,and GSH content in liver homogenates,were measured using biochemical assay kits.The levels of inflammatory cytokines IL-6,TNF-α,and IL-1β in serum were detected by ELISA.Liver pathological changes were assessed by HE staining,while mitochon-drial and autophagosome structures in liver tissues were observed using transmission electron microscopy.The number of PCNA-positive cells in liver tissues was e-valuated using immunohistochemical staining.The pro-tein expression levels of LC3Ⅱ,p62,Bax,Bcl-2,PC-NA,and CyclinD1 in liver tissues were determined by Western blot.Results LIRA pretreatment can im-prove the general condition of mice with acetamino-phen-induced liver injury(AILI),reduce serum ALT and AST levels,and effectively ameliorate the appear-ance and morphology of the liver as well as the patho-logical damage to liver tissue.Simultaneously,the lev-els of inflammatory cytokines IL-6,TNF-α,and IL-1βare significantly decreased;SOD activity and GSH con-tent are significantly increased,while MDA content is significantly reduced.Transmission electron microsco-py observations reveal the presence of numerous auto-phagosomes in the cytoplasm of liver tissue.Immuno-histochemical staining results indicate a significant in-crease in the number of PCNA-positive cells.Further-more,the expression of LC3Ⅱ,Bcl-2,PCNA,and Cy-clinD1 proteins in liver tissue is significantly upregulat-ed,while the expression of p62 and Bax proteins is significantly downregulated.However,after interven-tion with the autophagy inhibitor 3-MA,the aforemen-tioned protective effects of LIRA are significantly.Conclusions LIRA pretreatment can significantly im-prove liver injury in AILI mice.Its protective mecha-nism may be related to enhancing autophagy in hepato-cytes,thereby reducing oxidative stress,inflammatory response and apoptosis in liver of AILI mice.

Objective:In the context of China's cardiovascular disease(CVD)high-risk population screening and intervention project,this study systematically evaluates the applicability of the EQ-5D-5L and SF-6Dv2 instruments among individuals at high risk of CVD.Methods:Convergent validity was assessed using Spearman's correlation coefficient.Measurement agreement was evaluated through intraclass correlation coefficients(ICC)and Bland-Altman plots.Factors influencing utility differences were explored using multiple linear regression analysis.Kruskal-Wallis test and t-test were used to examine discriminant validity.Sensitivity was compared by effect size(ES),relative efficiency(RE),and the area under the receiver operating characteristic curve(ROC-AUC).Floor and ceiling effects were also compared.Results:Among 5,415 individuals at high risk of CVD,the two instruments showed moderate overall correlation and acceptable convergent validity,but dimension-specific correlations were weak,and measurement consistency was low(ICC=0.367).Both instruments effectively distinguished different health states,yet the SF-6Dv2 demonstrated superior sensitivity and a milder ceiling effect.Conclusion:When measuring the health utility value of CVD patients,scale selection should be cautious,especially for high-risk groups,and SF-6Dv2 is more appropriate.