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 compare the clinical efficacy of intraoperative intravenous tirofiban versus preoperative loading dose dual antiplatelet therapy in the acute phase LVIS stent-assisted coil embolization treatment for ruptured wide-necked intracranial aneurysms.Methods Patients with acutely ruptured,wide-neck intracranial aneurysms underwent LVIS stent-assisted coil embolization in the Department of Neurosurgery at Heze Municipal Hospital were retrospectively and consecutively enrolled from January 2017 to June 2023.According to the Chinese expert consensus on antiplatelet therapy for intracranial aneurysms,patients were divided into two groups based on the types of antiplatelet therapy they received:the loading-dose dual antiplatelet therapy(DAPT)group and the tirofiban group.Baseline and clinical data were collected and compared between the two groups,including age,sex,hypertension,diabetes mellitus,coronary artery disease,history of cerebral hemorrhage,preoperative Hunt-Hess grade,maximum aneurysm diameter,aneurysm neck width,and aneurysm location.Perioperative ischemic and hemorrhagic complications were collected and compared between the two groups.Perioperative ischemic complications included:intraoperative stent thrombosis(defined as filling defects in the parent artery,and,occlusion of the parent artery or stented branch during the procedure),and symptomatic ischemic infarction within 24 h postoperatively(confirmed by imaging with corresponding neurological deficits).Perioperative hemorrhagic complications included:intraoperative rupture of the target aneurysm(contrast extravasation or acute hemorrhage during embolization)and intracranial hemorrhage within 24 h postoperatively(new or worsened subarachnoid hemorrhage or intraparenchymal hemorrhage on CT).Clinical outcomes at 90 days were collected via telephone or outpatient follow-up,and evaluated using favorable prognosis defined as modified Rankin scale(mRS).A mRS score of 0-2 were defined as favorable prognosis and 3-6 as poor prognosis.Six-month postoperative imaging follow-up were collected,angiographic outcomes were categorized into four groups based on comparison with immediate post-embolization results:complete occlusion,total absence of contrast filling in the aneurysm sac;improved,reduced contrast filling;stable,unchanged contrast filling;and,recurrence,increased contrast filling.Results Totals of 108 patients with intracranial aneurysms treated by LVIS stent-assisted coiling were enrolled,with 30 males and 78females,aged32-75years(median age63[50,66]years).Among the108cases,55cases were assigned into the DAPT group,and 53 cases were included in the tirofiban group.(1)No statistically significant differences were observed between the tirofiban group and the DAPT group in baseline and clinical characteristics(all P＞0.05).(2)All patients underwent successful LVIS stent-assisted coiling,with a technical success rate of 100%.The total perioperative ischemic complications were 12.0%(13/108),including 4.6%(5/108)intraoperative stent thrombosis and 7.4%(8/108)symptomatic ischemic infarction within 24h after surgery.The total perioperative hemorrhagic complications rate was 1.9%(2/108),including 1 case of intraoperative aneurysm rupture and 1 case of postoperative intracranial hemorrhage within24h.92.6%(100/108)of the patients exhibited favorable prognosis and 7.4%(8/108)showed poor prognosis at the 90-day follow-ups.78.7%(85/108)of the patients accomplished at 6-month imaging follow-ups,the complete occlusion ratio was 94.1%(80/85)and the recurrence ratio was 2.4%(2/85).(3)The overall perioperative ischemic complication rates were 13.2%(7/53)in the tirofiban group and 10.9%(6/55)in the DAPT group,with no statistically significant difference(P=0.720).Intraoperative stent thrombosis occurred more frequently in the DAPT group(9.1%[5/55]vs.0,P=0.025),while symptomatic ischemic infarction within 24 h post-procedure was lower in the DAPT group(1.8%[1/55]vs.13.2%[7/53],P=0.028).The hemorrhagic complications occurred only in the DAPT group,with a rate of 3.6%(2/55),while no events observed in the tirofiban group.At the 90-day follow-up,the proportion of patients with favorable outcomes was 94.3%(50/53)in the tirofiban group and 90.9%(50/55)in the DAPT group,with no statistically significant difference between the groups(P=0.754).Conclusions Both intraoperative intravenous tirofiban and preoperative loading-dose DAPT demonstrated comparable safety profile and favorable clinical efficacy in the acute-phase treatment of ruptured wide-necked intracranial aneurysms with LVIS stent-assisted coil embolization.The results require further validation through large-scale prospective studies.

The"capitation payment with retained surplus and shared accountability for reasonable overruns"mechanism constitutes a pivotal institutional framework for advancing the high-quality development of County Medical Communities(CMCs).This study addresses two critical operational challenges:identifying the sources of medical insurance fund surplus and optimizing the governance of fund retention processes.Grounded in transaction cost theory,we develop an analytical framework examining the formation of medical insurance fund surplus through the dual lenses of intra-organizational dynamics within CMCs and external medical insurance payment mechanism design.Utilizing Deqing County,Zhejiang Province as an empirical case,this research proposes a five-pronged strategy:Clarifying generation channels of insurance fund surplus,scientifically determining regional medical insurance budgets,implementing bundled payment mechanisms for CMCs,adopting hybrid payment models integrating unified and differentiated approaches,and establishing performance-based incentive systems.These findings elucidate the formative pathways of medical insurance fund surplus while offering theoretical and practical insights for enhancing payment system reforms to support CMC development.

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.