Background The quality of occupational health technical services is directly linked to the protection of workers' health rights and the efficacy of occupational disease prevention and control. However, the industry still faces critical challenges: sporadic instances of institutional non-compliance and persistent irregularities in professional practice continue to undermine overall service performance. Objective To assess the current quality status of occupational health technical services in Zhejiang Province and propose countermeasures for quality improvement, providing a scientific basis for policy optimization and service delivery quality enhancement. Methods A total of 69 occupational health technical service institutions in Zhejiang Province that obtained formal accreditation as of April 30, 2024, were sampled, including 3 public institutions and 66 private institutions (comprising 3 formerly Class-A, 28 formerly Class-B, 11 formerly Class-C, and 24 newly certified institutions). Following the Technical Protocol for Quality Monitoring of Occupational Health Technical Service in Zhejiang Province and the Technical Protocol for Proficiency Testing of Occupational Health Detection in Zhejiang Province, a quality assessment task force comprising national and provincial experts was established. Evaluation was conducted across four dimensions: qualification maintenance and compliance, standardization of technical services, authenticity of technical services, and proficiency testing, utilizing a combination of document review, on-site inspections, and technical skill assessments. Results The occupational health technical service institutions in Zhejiang Province were predominantly private entities (82.5%), with significant disparities in overall service quality. The pass rates for qualification maintenance and compliance, technical service standardization, technical service authenticity, and the excellence rate for laboratory proficiency testing were 81.5%, 80.7%, 97.3%, and 90.4%, respectively. Regarding qualification maintenance, the pass rates for "environmental conditions" (49.8%, 56.7%) and "instrumentation and equipment" (58.2%、65.6%) were significantly lower for formerly Class-C and newly certified institutions compared to other categories. In terms of technical standardization, "standardized on-site inspections" recorded the lowest pass rate (67.4%), with newly certified institutions at only 48.0%. Regarding technical service authenticity, formerly Class-C institutions exhibited issues such as missing raw chromatograms for blank samples (85.7% pass rate). In laboratory proficiency testing, public and formerly Class-A institutions achieved 100% excellence rates, but the performance of formerly Class-C and newly certified institutions was comparatively weak; specifically, the failure rate for organic analysis in formerly Class-C institutions reached 20%; the failure rate for dust testing items in newly certified institutions was 10.3%. Conclusion The overall quality of occupational health technical services in Zhejiang Province still requires significant improvement, particularly in basic institutional conditions, the standardization of on-site inspections, and laboratory proficiency in organic and dust analysis. Formerly Class-C and newly certified institutions should be the primary focus of quality management efforts. Differentiated regulatory strategies are recommended, alongside strengthening interim and ex-post supervision to gradually enhance the quality of occupational health technical services across all institutions.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective To explore the clinical features, diagnosis, treatment and prognosis of testicular Ewing sarcoma(ES), so as to enhance the understanding and differential diagnosis of this disease. Methods A retrospective analysis was conducted on the clinical manifestations, auxiliary examinations and pathological findings of a case of left-sided ES treated at our hospital, supplemented by a review of relevant literature. Results A 24-year-old male patient presented with left testicular enlargement with pain. Magnetic resonance imaging suggested a neoplastic lesion, with a high likelihood of testicular germ cell tumor. A radical left orchiectomy was performed. Postoperative pathology revealed testicular ES. The patient subsequently completed 8 cycles of VDC/IE chemotherapy and showed no evidence of recurrence at 10 months of follow-up. Analysis of literature on 5 previously reported cases of testicular ES had testicular enlargement as the primary presentation. All patients underwent surgery and 4 received adjuvant therapy. Follow-up revealed 1 death at 9 months postoperatively, with no recurrence or progression seen in the remainder. Conclusion ES occurring in the testis is extremely rare.Testicular ES is a poorly differentiated small round-cell malignant tumor that primarily presents as testicular enlargement. Current treatment mainly surgical resection combined with adjuvant chemotherapy.

Morinda officinalis is one of the “Four Great Southern Medicines” and is commonly used in clinical practice to treat erectile dysfunction and nocturnal emissions caused by kidney yang deficiency， uterine cold infertility， menstrual disorders， low abdominal pain， rheumatism， and muscle and bone weakness. Based on the principle of “treating raw and cooked differently”， this article summarizes the effects of different processing methods on the chemical constituents and pharmacological effects of M. officinalis . Research has found that processing can affect the content and structure of sugars， iridoids， and anthraquinones in M. officinalis ， thereby regulating its pharmacological effects such as anti-osteoporosis， immune regulation， anti-fatigue， and anti-depression. Among them， salt M. officinalis shows outstanding performance in anti-osteoporosis and enhancing immunity. Although previous studies have preliminarily elucidated the major chemical constituents of M. officinalis and their pharmacological characteristics， the dynamic transformation paths of constituents during the processing remain to be further clarified， especially the es tablishment of a quality marker system associated with the processing technology， to achieve the precision and intelligent development of the processing.

Morinda officinalis is one of the “Four Great Southern Medicines” and is commonly used in clinical practice to treat erectile dysfunction and nocturnal emissions caused by kidney yang deficiency， uterine cold infertility， menstrual disorders， low abdominal pain， rheumatism， and muscle and bone weakness. Based on the principle of “treating raw and cooked differently”， this article summarizes the effects of different processing methods on the chemical constituents and pharmacological effects of M. officinalis . Research has found that processing can affect the content and structure of sugars， iridoids， and anthraquinones in M. officinalis ， thereby regulating its pharmacological effects such as anti-osteoporosis， immune regulation， anti-fatigue， and anti-depression. Among them， salt M. officinalis shows outstanding performance in anti-osteoporosis and enhancing immunity. Although previous studies have preliminarily elucidated the major chemical constituents of M. officinalis and their pharmacological characteristics， the dynamic transformation paths of constituents during the processing remain to be further clarified， especially the es tablishment of a quality marker system associated with the processing technology， to achieve the precision and intelligent development of the processing.

OBJECTIVE To study the mechanism of action of Rehmannia glutinosa-medicated serum （RG） in regulating lipopolysaccharide （LPS）-induced polarization of BV2 microglia to interfere with neuroinflammation. METHODS An in vitro neuroinflammation model of BV2 cells induced by LPS was established. Cells were divided into control group， LPS group， 10%RG group， 20%RG group， BAY 11-7082 [nuclear factor kappa B （NF-κB） inhibitor] group， and 20%RG+BAY 11-7082 group. Except for the control group， cells in all other groups were pretreated with corresponding drugs for 12 h and then stimulated with LPS for 24 h. The morphology of cells in each group was observed， and the fluorescence intensity of ionized calcium binding adaptor molecule 1 （Iba-1）， inducible nitric oxide synthase （iNOS）， CD206， the mRNA expression of tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， IL-1β， iNOS， IL-4， IL-10， transforming growth factor-β （TGF-β）， arginase-1 （Arg-1）， and the Toll-like receptor 4 （TLR4）/NF-κB/NOD-like receptor thermal protein domain associated protein 3 （NLRP3） signaling pathway-related protein expression were detected. RESULTS Compared with the control group， the LPS group showed significant changes in cell morphology， exhibiting a typical M1 activated morphology. The fluorescence intensity of Iba-1 and iNOS， the relative expression levels of TNF-α， IL-6， IL-1β， iNOS mRNA， the relative expression levels of TLR4， NLRP3， caspase-1 p20， IL-1β p17 protein， and the phosphorylation levels of NF-κB p65 and inhibitor of nuclear factor kappa B α were significantly increased （P＜0.05）. The fluorescence intensity of CD206 and the relative expression levels of IL-4， IL-10， TGF-β， and Arg-1 mRNA were significantly reduced （P＜0.05）. Compared with the LPS group， the changes in the above indicators in the cells of the 10%RG group， 20%RG group， BAY 11-7082 group， and 20%RG+BAY 11-7082 group were significantly reversed （P＜0.05）， and there was no statistically significant difference in the comparison of the last three groups （P＞0.05）. CONCLUSIONS RG can effectively inhibit the overall activation of microglia and correct their M1/M2 polarization imbalance， and its mechanism may be closely related to the inhibition of excessive activation of the TLR4/NF-κB/NLRP3 signaling pathway.

OBJECTIVE To investigate the current status of informatization tools applied to antimicrobial stewardship in county hospitals in China， and to provide a reference for promoting the informatization development of antimicrobial stewardship in county hospitals.METHODS From August to September 2025， an online questionnaire survey was conducted among 1 007 county hospitals across 31 provinces（autonomous regions， and municipalities） in China. The survey focused on the application of four types of informatization tools：the real-time monitoring system for antimicrobial use， the pre-prescription review system， the irrational use warning system， and the electronic prescription evaluation system（hereinafter referred to as the “monitoring system”“review system”“warning system”“evaluation system”，respectively）. Correlation analysis and binary Logistic regression analysis were employed to explore the influencing factors.RESULTS Of the county hospitals， 80.83% were equipped with at least one type of informatization tool for antimicrobial stewardship， while 19.17% had no relevant tool. Only 18.87% of the hospitals were equipped with all four types of tools. The monitoring system exhibited the highest equipped rate （70.80%）， whereas the warning system was equipped in only 33.96% of the hospitals. Four types of tools are evenly and adequately deployed in the eastern region， the central and western regions have far lower deployment rates of intervention tools including review system and warning system， accompanied by prominent intra-provincial gaps. The total number of the health technicians， hospital grade， and prescription review rate of outpatient antibacterial drug were significantly and positively correlated with the deployment rates of the four informatization tools （ P ＜0.05）. The results of the Logistic regression model fitting indicated that the model of the review system was rated “excellent” [area under the receiver operating characteristic curve （AUC）＝0.81]， and the model of the warning system was rated “further improvement” （AUC＝0.67）. The total number of health technicians was significantly positively correlated with the deployment rates of all four types of informatization tools （all P ＜0.05）.CONCLUSIONS The informatization of antimicrobial stewardship in county hospitals in China remains in the initial stage of transitioning from “passive statistics” to “active prevention and control”， with challenges such as structural imbalance and regional disparities. In the future， differentiated support strategies should be implemented， the allocation of resources should be optimized， to enhance the level of refined antimicrobial stewardship.

ObjectiveTo explore the changes in volatile components， total polysaccharides， enzyme activity， and chromaticity value of Myristicae Semen Koji（MSK） during the fermentation process， and conduct correlation analysis. MethodsBased on gas chromatography-mass spectrometry（GC-MS）， the changes of volatile components in MSK at different fermentation times were identified. The phenol sulfuric acid method， dinitrosalicylic acid method（DNS）， and carboxymethyl cellulose sodium salt method（CMC-Na） were used to investigate the total polysaccharide content， amylase activity， and cellulase activity during the fermentation process. Visual analysis technology was used to explore the changes in chromaticity values， revealing the fermentation process of MSK and the dynamic changes of various measurement indicators， partial least squares-discriminant analysis（PLS-DA） was used to explore the differential compounds of MSK at different fermentation degrees， and Pearson correlation analysis was used to explore the correlation between volatile components of MSK and total polysaccharides， enzyme activity， and chromaticity values. ResultsA total of 60 volatile compounds were identified from MSK， the relative contents of components such as （+）-α-pinene， β-phellandrene， β-pinene， （+）-limonene， and p-cymene obviously increased， while the relative contents of components such as safrole， methyl isoeugenol， methyleugenol， myristicin， and elemicin significantly decreased. During the fermentation process， the total polysaccharide content showed an upward trend， while the activities of amylase and cellulase showed an initial increase followed by a decrease， and reached their maximum value at 40 h. the overall brightness（L^*） and total color difference（ΔE^*） gradually increased， while the changes in red-green value（a^*） and yellow-blue value（b^*） were not obvious. PLS-DA results showed that MSK could be clearly distinguished at different fermentation times， and 13 differential biomarkers were screened out. Pearson correlation analysis results showed that the contents of α-terpinene， β-phellandrene， methyleugenol， β-cubebene and myristic acid had an obvious correlation with chromaticity values. ConclusionAfter fermentation， the volatile components， total polysaccharides， amylase activity， and cellulase activity of MSK undergo significant changes， and there is a clear correlation between them and chromaticity values， which reveals the dynamic changes in the fermentation process and related indicators of MSK， laying a foundation for the quality control.

ObjectiveTo investigate the protective effect of Rehmanniae Radix iridoid glycosides （RIG） on the kidney tissue of streptozotocin （STZ）-induced diabetic mice and explore the underlying mechanism. MethodsTwelve of 72 male C57BL/6J mice were randomly selected as the normal group， and the remaining 60 mice were fed with a high-fat diet for six weeks combined with injection of 60 mg·kg^-1 STZ for 4 days to model type 2 diabetes mellitus. The successfully modeled mice were randomized into model， metformin （250 mg·kg^-1）， catalpol （100 mg·kg^-1）， low-dose RIG （RIG-L， 200 mg·kg^-1） and high-dose RIG （RIG-H， 400 mg·kg^-1） groups （n=11）. Mice in each group were administrated with corresponding drugs， while those in the normal group and model group were administrated with the same dose of distilled water by gavage once a day. After 8 weeks of intervention， an oral glucose tolerance test （OGTT） was performed， and the area under the curve （AUC） was calculated. After mice were sacrificed， both kidneys were collected. The body weight， kidney weight， and fasting blood glucose （FBG） were measured. Biochemical assays were performed to measure the serum levels of triglycerides （TG）， total cholesterol （TC）， serum creatinine （SCr）， and blood urea nitrogen （BUN）. Enzyme-linked immunosorbent assay （ELISA） was employed to determine the serum level of fasting insulin （FINS）， and the insulin sensitivity index （ISI） and homeostatic model assessment for insulin resistance （HOMA-IR） were calculated. The pathological changes in kidneys of mice were observed by hematoxylin-eosin staining and Masson staining. The immunohistochemical method （IHC） was employed to assess the expression of interleukin-1 （IL-1）， interleukin-6 （IL-6）， tumor necrosis factor-α（TNF-α）， transforming growth factor-β₁ （TGF-β₁）， and collagen-3 （ColⅢ） in the kidney tissue. The protein levels of TGF-β₁， cell signal transduction molecule 3 （Smad3）， matrix metalloproteinase-9 （MMP-9）， and ColⅢ in kidneys of mice were determined by Western blot. ResultsCompared with the normal group， the model group showcased decreased body weight and ISI （P<0.01）， increased kidney weight， FBG， AUC， FINS， HOMA-IR， TC， TG， SCr， and BUN （P<0.01）， glomerular hypertrophy， capsular space narrowing， and collagen deposition in the kidney， up-regulated protein levels of IL-1， IL-6， TNF-α， TGF-β₁， ColⅢ， and Smad3 （P<0.01）， and down-regulated protein level of MMP-9 （P<0.01） in the kidney tissue. Compared with the model group， the treatment groups had no significant difference in the body weight and decreased kidney weight （P<0.05， P<0.01）. The FBG level declined in the RIG-H group after treatment for 4-8 weeks and in the metformin， catalpol， and RIG-L groups after treatment for 6-8 weeks （P<0.01）. The AUC in the RIG-L， RIG-H， and metformin groups decreased （P<0.05， P<0.01）. The levels of TC， SCr， and BUN in the serum of mice in each treatment group became lowered （P<0.05， P<0.01）. The level of TG declined in the RIG-L， RIG-H， and metformin groups （P<0.05， P<0.01）. The serum level of FINS declined in the catalpol， RIG-L， and metformin groups （P<0.01）. Compared with the model group， the treatment groups showed decreased HOMA-IR （P<0.01）， increased ISI （P<0.01）， alleviated pathological changes in the kidney tissue， and down-regulated expression of IL-1 and TGF-β₁. In addition， the protein levels of IL-6， TNF-α， and ColⅢ in the RIG-H and metformin groups and IL-6 and TNF-α in the RIG-L group were down-regulated （P<0.05， P<0.01）， and the protein levels of IL-6， TNF-α， and ColⅢ in the catalpol group and ColⅢ in the RIG-L group showed a decreasing trend without statistical difference. The protein levels of TGF-β₁， Smad3， and ColⅢ in the RIG-H and metformin groups were down-regulated （P<0.01）. Compared with that in the model group， the protein level of MMP-9 was up-regulated in each treatment group （P<0.01）. ConclusionRIG can improve the renal structure and function of diabetic mice by regulating the TGF-β₁/Smads signaling pathway.