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 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 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 fabricate wall-less vascular tissue mimicking materials(TMM)with different tube diameters that match the hemodynamic parameters of human carotid arteries,and to investigate their hemodynamic characteristics.Methods TMM with different diameters and blood mimicking fluids containing scattering particles were fabricated.The variation laws of hemodynamic parameters under different flow velocities and TMM phantom diameters were verified.Key hemodynamic parameters including peak systolic velocity(PSV),end-diastolic velocity(EDV),and resistance index were measured using Doppler ultrasound,and their clinical application value in carotid artery diseases was evaluated.Results The fabricated samples exhibited a sound velocity of(1506.2±0.1)m/s and an attenuation of(0.76±0.01)dB/cm,and the vascular diameters were 4.0 and 6.0 mm,which corresponded to the normal clinical range of the external and internal carotid arteries,respectively.For the 4.0 mm TMM,both PSV and EDV were linearly correlated with flow velocity(R2=0.77,P<0.001;R2=0.74,P=0.001),and Pearson correlation analysis confirmed strong positive correlations(r=0.89,95%CI:0.82-0.93;r=0.94,95%CI:0.90-0.97,all P<0.001).For the 6.0 mm TMM,PSV and EDV also demonstrated significant linear correlations with flow velocity(R2=0.70,P=0.001;R2=0.61,P=0.005),with Pearson correlation analysis revealing strong positive correlations(r=0.86,95%CI:0.78-0.91;r=0.79,95%CI:0.68-0.87).All the data were consistent with hemodynamic parameters and followed the variation law of hemodynamic parameters.Conclusion The fabricated TMM and blood mimicking fluids meet the requirements for clinical ultrasound research on hemodynamics,and their material ratios can be used as a reference for the subsequent researches with diverse objectives.

Objective To analyze the clinical value of fungus(1,3)-β-D glucan test magnetic particle chemiluminescence immunoas-say(G-CLIA)for diagnosis of invasive fungal disease(IFD).Methods A total of 509 patients with clinically suspected IFD in Qilu Hospital of Shandong University from 1 March to 30 April,2023 were collected.According to the inclusion criteria,the 509 patients were grouped into IFD group(141 patients)and non-IFD group(368 patients).The sensitivity,specificity,accuracy,positive predic-tive value and negative predictive value of G-CLIA were analyzed,and the consistency of the results of G-CLIA with G test colorimetric method,fungal smear microscopy or culture and metagenomics next-generation sequencing(mNGS)was comparatively analyzed.Re-sults The sensitivity and specificity of G-CLIA were 88.65％and 96.47％,respectively,and the positive percentage agreement of G-CLIA with G test colorimetric assay,fungal smear microscopy or culture,and mNGS were 92.19％,75.86％,and 75.00％,respective-ly,and the consistency of G-CLIA with G test colorimetric assay was the highest(kappa value≥ 0.75).Conclusion G-CLIA has high sensitivity and specificity for detecting IFD with excellent diagnostic value.Combined with the fully automated chemiluminescence analy-zer,G-CLIA test is fast and has a high throughput,which provides a new option for the clinical diagnosis of IFD.

AIM:To explore the potential of Huangqi sanqi mixture(AP)in improving renal fi-brosis by performing transcriptome sequencing of the kidneys of the unilateral ureteral ligation mouse group and the Huangqi sanqi mixture inter-vention group,and using bioinformatics to verify the signitficantly different lncRNAs mechanism.METHODS:Twenty-four C57 mice were divided in-to sham operation group,renal fibrosis group,Huangqi sanqi mixture intervention group(3.944 g/kg)and irbesartan positive control intervention group,with 6 mice in each group.A mouse model of renal fibrosis was established by unilateral ure-teral ligation(UUO).The animals were given intra-gastric administration after operation,and the ani-mals were sacrificed and the specimens were col-lected after seven consecutive days of administra-tion.The changes of Huangqi sanqi mixture on re-nal fibrosis pathological damage were analyzed by HE and Masson staining,and the protein levels of Fn and α-SMA in renal tissue of each group were detected by Western blot and immunohistochemis-try to evaluate the alleviating effect of Huangqi san-qi mixture on renal fibrosis.Subsequently,lncRNA expression information was obtained by transcrip-tome sequencing,and Quantitative Real-time PCR(qPCR)was performed after data quality,GO en-richment and differential lncRNA were analyzed.According to the differential lncRNA and target analysis results obtained by sequencing,lncRNA Gm51500/Adam12 was overexpressed in vitro,and its mechanism in the protection of renal fibrosis by Huangqi sanqi mixture was studied by immunohis-tochemistry,immunofluorescence staining and qP-CR verification.RESULTS:Compared with the mod-el group,the renal fibrosis of the mice in the Huangqi sanqi mixture intervention group was sig-nificantly reduced,and the protein levels of α-SMA and Fn and the expression of lncRNA in the renal tis-sue were significantly down-regulated(P＜0.000 1).Three lncRNAs were screened and verified to in-crease in the model group and significantly de-crease after AP intervention,namely lncRNA Gm29994,Gm51500 and Gm35391.Target analysis showed that lncRNA Gm51500 had the most signifi-cant relationship with Adam12.The results of ani-mal experiments showed that Adam12 was highly expressed in the kidney of UUO mice and was sig-nificantly inhibited after AP intervention.Subse-quent cell experiments confirmed that overexpres-sion of lncRNA Gm51500 could up-regulate TGF-β-induced renal tubular cell fibrosis and Adam12 ex-pression.Cell recovery experiments confirmed that Adam12 overexpression reversed the inhibitory ef-fect of AP on renal tubular cell injury and fibrosis.CONCLUSION:Huangqi sanqi mixture can improve renal fibrosis.Based on transcriptomic sequencing,lncRNA Gm51500/Adam12 axis may be a potential target for Huangqi sanqi mixture to improve renal fibrosis.

Objective To study the medication law of Professor Wang Zhongyi in the treatment of tic disorder(TD)based on data mining technology.Methods From January 1,2022 to December 31,2023,the cases treated for TD in Professor Wang Zhongyi's outpatient clinic,which participated in the real-world study were collected.A comprehensive database has been established,screening information related to effective case diagnosis and treatment.Utilizing Excel 2021,R 4.4.2,Origin 2024 and Cytoscape 3.9.1,this study conducted medication frequency analysis,property-flavor-meridian tropism analysis,efficacy analysis,association rule analysis,clustering analysis and co-occurrence network analysis to summarize medication law.Results Totally 640 effective prescriptions were included,involving 208 kinds of Chinese materia medica.The properties were mainly warm,cold,and neutral.The flavors were mainly pungent,bitter and sweet.The meridians were mainly liver meridians.The therapeutic categories were primarily composed of liver-calming and endogenous wind-stopping drugs,along with exterior-resolving prescriptions.Correlation analysis obtained 17 strongly correlated rules.Clustering analysis obtained 5 types of medicinal combinations.The therapeutic categories were primarily composed of liver-calming and endogenous wind-stopping drugs,along with exterior-resolving prescriptions.Conclusion According to the comprehensive statistical analysis,Uncariae Ramulus cum Uncis,Gastrodiae Rhizoma,Haliotidos Concha,Paeoniae Radix alba,Bupleuri Radix,Puerariae Lobatue Radix and Scorpio are the core drugs used by Professor Wang Zhongyi to treat TD.Professor Wang Zhongyi believes that the core pathogenesis of TD is the internal movement of liver wind,and the treatment mainly focuses on calming the liver,calming the wind and stopping spasms,while also nourishing the heart,calming the mind,harmonizing blood and relieving qi.Based on different clinical symptoms of TD,modifications and adjustments are made to the core prescription to treat children with TD.