The c-Jun N-terminal kinase （JNK） signaling pathway， a key member of the mitogen-activated protein kinase （MAPK） family， plays a central role in the pathogenesis and progression of central nervous system （CNS） diseases by regulating core biological processes such as apoptosis， inflammatory responses， synaptic plasticity， and autophagy. This article sorts out and analyzes relevant literature published domestically and internationally in recent years， summarizing the mechanisms of action of the JNK signaling pathway in common CNS diseases and the research progress in traditional Chinese medicine （TCM） interventions in CNS diseases through the regulation of the JNK signaling pathway. Studies have shown that active components of TCM， such as berberine， paeoniflorin， and astragaloside Ⅳ， as well as compound formulations like Heixiaoyao san， Ditan tang， and Buyang huanwu tang， can exert neuroprotective effects in various CNS disorders， including Alzheimer’s disease， Parkinson’s disease， cerebral ischemia-reperfusion injury， and epilepsy， by inhibiting the aberrant activation of the JNK signaling pathway， thereby alleviating neuroinflammation， oxidative stress， and neuronal apoptosis， while improving synaptic function and cognitive behavioral deficits， regulating autophagy， and maintaining blood-brain barrier integrity.

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: To explore the prenatal and postnatal phenotypes of 22q11.2 microdeletion syndrome (22q11.2DS) and enhance clinical understanding of this condition. METHODS: Data were collected from 86 fetuses diagnosed with 22q11.2DS at four prenatal diagnostic centers across China between January 2014 and August 2025. Prenatal imaging findings, pregnancy outcomes, and postnatal conditions were analyzed. RESULTS: Among the 86 fetuses, complete ultrasound data were available for 65 cases. Cardiovascular abnormalities were observed in 42 cases, thymic hypoplasia or aplasia in 7 cases, urinary system anomalies in 6 cases, nuchal translucency (NT) thickening in 7 cases, butterfly vertebrae, clubfoot, omphalocele and diaphragmatic hernia in 1 case each, cleft lip and palate in 2 cases, and ultrasound soft markers in 13 cases. The parents of 9 fetuses opted to continue with the pregnancy. Among these, 6 showed no significant ultrasound abnormalities and no related phenotypes postnatally, while the remaining 3 exhibited ultrasound anomalies with postnatal manifestations including developmental delay, immunodeficiency, and cardiac defects. CONCLUSION Fetuses with 22q11.2DS may exhibit various ultrasound abnormalities in multiple systems before and after birth. In addition to cardiovascular anomalies, they may also present with thymic hypoplasia or aplasia, thickened NT, and urinary abnormalities. Fetuses with thickened NT or thymic anomalies should be closely monitored, and thymic assessment should be included in routine prenatal imaging evaluations. For fetuses with 22q11.2DS who show no ultrasound abnormalities, the risk of developing severe phenotypes after birth is relatively low, but occult palate clefts and psychiatric disorders cannot be ruled out. Due to limitations in sample size and follow-up duration, above conclusions require further validation through large-scale prospective studies.
Humans ; Female ; Pregnancy ; Ultrasonography, Prenatal ; DiGeorge Syndrome/genetics* ; Adult ; Male ; Follow-Up Studies ; Fetus/diagnostic imaging* ; Phenotype ; Infant, Newborn

Prostate cancer is one of the most common malignant tumors of male genitourinary system. Prostate cancer has the following characteristics： insidious onset， early asymptomatic or not obvious symptoms， complex etiology and pathogenesis， long incubation period and so on. Therefore， the realization of its early diagnosis and treatment is of great significance to the prognosis of patients. Prostate-specific membrane antigen （PSMA） is a type 2 transmembrane glycoprotein that is highly expressed on the membrane of almost all primary and metastatic prostate cancer cells， and is an ideal target for prostate cancer imaging and treatment. In recent years， with the approval of urea-containing small molecule PET （positron emission computed tomography） radiopharmaceutical based on PSMA （⁶⁸Ga-PSMA-11， ¹⁸F-PSMA-1007）， PET-CT （positron emission computed tomography/computed tomography） has shown new potential for early diagnosis and accurate staging of prostate cancer patients. This review mainly summarizes the research progress of urea-containing PSMA PET imaging agents and finds that they have defects such as uptake in non-target tissues like the kidneys， lacrimal glands， and salivary glands. Thus， further optimizing their structure to reduce the uptake in non-target tissues， providing provide convenience for the labeling of therapeutic radiopharmaceuticals， thereby achieving the goal of integrated diagnosis and treatment， is an important development direction in this field.

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.

ObjectiveTo construct and validate a clinical prediction model for inflammatory remission outcomes in rheumatoid arthritis （RA） patients with liver and kidney deficiency syndrome treated with Bushen Zhiwang Decoction （补肾治尪汤， BZD） based on metabolomics. MethodsA prospective cohort study was conducted， enrol-ling 60 RA patients with liver and kidney deficiency syndrome. All patients were treated with BZD and conventional-dose oral conventional synthetic disease-modifying antirheumatic drugs （csDMARDs） for 12 months. Clinical data were collected， and the change in disease activity score in 28 joints （DAS28） after treatment compared with baseline （△DAS28） was used as the primary outcome and grouping criterion. Peripheral blood samples were collected before treatment to analyze plasma metabolites. Differential analysis and least absolute shrinkage and selection operator （LASSO） regression were used to preliminarily screen differential metabolites， followed by machine learning algorithms to further identify a core metabolite combination. Based on the expression levels of the core metabolite combination， a novel metabolite index， namely the metabolomics-based inflammatory remission score （Met-IRS）， was calculated using standar-dized metabolite values， and its clinical applicability was evaluated. A clinical prediction model was constructed by integrating clinical characteristics and Met-IRS， and the model performance was assessed. ResultsAmong the 60 patients， those with △DAS28 ≥ 0.27 were assigned to the high inflammatory remission group， while those with △DAS28 ＜ 0.27 were assigned to the low inflammatory remission group， with 30 cases in each group. Compared to the low inflammatory remission group， the high inflammatory remission group showed a higher frequency of methotrexate use and a lower positive rate of rheumatoid factor （RF） （P＜0.05）. Seven core metabolites were identified as the optimal combination， including mangiferic acid， fatty acid-hydroxy fatty acid ester 40∶6， fatty acid-hydroxy fatty acid ester 18∶0， fatty acid-hydroxy fatty acid ester 36∶1， glucosylceramide， lysophosphatidylcholine 22∶5， and pregnanetriol ketone. The calculated Met-IRS comprehensively reflected the characteristics of differential metabolites and demonstrated clinical applicability. Met-IRS was significantly higher in the high inflammatory remission group than in the low inflammatory remission group， and was positively correlated with high inflammatory remission outcomes （P＜0.05）. Based on the variables Met-IRS， methotrexate use， leflunomide use， and RF positivity， a clinical prediction model for inflammatory remission in RA treatment （Cj-RTRM） was constructed. Model performance evaluation demonstrated that the model had good clinical predictive ability， with an area under the receiver operating characteristic curve （AUC） of 0.880， sensitivity 0.967， specificity 0.700 and Youden's index 0.667. ConclusionThe clinical prediction model Cj-RTRM constructed based on the metabolomics-based inflammatory remission score Met-IRS can effectively predict clinical inflammatory remission outcomes in RA patients treated with BZD and accurately identify the advantageous population for this treatment. This model provides guiding evidence for dynamic inflammation monitoring， targeted management， and identification of populations with advantages in traditional Chinese medicine.

Alzheimer’s disease （AD） is an age-related neurodegenerative disorder. Marrow-sea insufficiency serves as the fundamental basis for the onset of AD. Early syndrome differentiation-based intervention helps to delay disease progression， and improve patients’ cognitive function. Qifu yin is a representative specialized prescription for AD with marrow-sea insufficiency syndrome. Studies demonstrate that Qifu yin exerts neuroprotective effects through multiple pathways， including inhibiting the abnormal deposition of amyloid β -protein and hyperphosphorylation of tau protein， alleviating neuroinflammation， regulating oxidative stress and mitochondrial dysfunction， modulating the cholinergic system， and improving synaptic plasticity. Qifu yin combined with Western medicine such as donepezil， memantine， and butylphthalide， or combined with external therapies such as acupuncture， can effectively improve cognitive function and activities of daily living in AD patients with favorable safety. Future research should focus on the core pathogenesis and key targets of AD with marrow-sea insufficiency syndrome， provide in-depth elucidation of the scientific connotation of Qifu yin’s “tonifying the kidney to produce marrow”， and further conduct high-quality clinical studies to provide scientific evidence for the prevention and treatment of AD with marrow-sea insufficiency syndrome.

Objective: To validate the analytical performance, operational performance, and process control measures of a domestic fully automatic nucleic acid testing (NAT) system, thereby ensuring an efficient and orderly blood screening workflow. Methods: The concordance rate and sensitivity of WanTag-Vortex Plus system were verified using WHO standard reference panels of HIV-1, HCV and HBV, while precision was assessed using weak positive samples of HIV-1, HCV and HBV. As for its operational performance evaluation, cross-contamination resistance was assessed using strong positive samples, and throughput and stress testing were conducted using negative samples. Reagent stability was verified using weak positive samples, and inter-system performance consistency was assessed using verification panels. In addition, the process control measures were verified using the laboratory quality control demand scale. Results: 1) Verification of concordance rate: The detection results of negative and positive samples of HIV-1, HCV and HBV by WanTag-Vortex Plus system were all consistent with expectations, and the concordance rate was 100%. 2) Precision verification: the repeatability and intermediate precision were extremely high, and the coefficient of variation was less than 5%. 3) Verification of analytical sensitivity: The detection limit of 95% for standard strains of HIV-1, HCV and HBV by WanTag-Vortex Plus system in our laboratory was consistent with the analytical sensitivity provided by reagent manufacturers. 4) Verification of cross-contamination resistance: Five strong positive samples and 87 negative samples were placed according to the actual working conditions and equipment operation design, and the test results were consistent with expectations, with no cross-contamination in the testing system. 5) Throughput and stress testing: Each system completed the individual donor-nucleic acid amplification testing (ID-NAT) of 276 samples in three batches within 12 hours, and successfully completed the ID-NAT test of 828 samples in three consecutive days. 6) Verification of reagent stability: After extreme storage (unsealed storage for 1 week with 4 freeze-thaw cycles), the reagents maintained 100% detection rate in the weak positive samples of HIV-1, HCV, and HBV, showing no significant differences from the control group (Kappa=1). 7) Verification of inter-system performance consistency: The system has stable operation performance, and the performance comparison results across the four devices were consistent (Kappa=1). 8) Process control measures: WanTag-Vortex Plus system software accurately controlled the equipment operation process with strict quality control measures, and correctly interpreted and safely reported the test results. Conclusion: The analytical and operational performance of the WanTag-Vortex Plus system complies with manufacturer design standards and essential laboratory workflow requirements. Integrated with laboratory information system (LIS), the system's control software meets standard process control requirements, yet requires further improvement.

Objective: To validate the analytical performance, operational performance, and process control measures of a domestic fully automatic nucleic acid testing (NAT) system, thereby ensuring an efficient and orderly blood screening workflow. Methods: The concordance rate and sensitivity of WanTag-Vortex Plus system were verified using WHO standard reference panels of HIV-1, HCV and HBV, while precision was assessed using weak positive samples of HIV-1, HCV and HBV. As for its operational performance evaluation, cross-contamination resistance was assessed using strong positive samples, and throughput and stress testing were conducted using negative samples. Reagent stability was verified using weak positive samples, and inter-system performance consistency was assessed using verification panels. In addition, the process control measures were verified using the laboratory quality control demand scale. Results: 1) Verification of concordance rate: The detection results of negative and positive samples of HIV-1, HCV and HBV by WanTag-Vortex Plus system were all consistent with expectations, and the concordance rate was 100%. 2) Precision verification: the repeatability and intermediate precision were extremely high, and the coefficient of variation was less than 5%. 3) Verification of analytical sensitivity: The detection limit of 95% for standard strains of HIV-1, HCV and HBV by WanTag-Vortex Plus system in our laboratory was consistent with the analytical sensitivity provided by reagent manufacturers. 4) Verification of cross-contamination resistance: Five strong positive samples and 87 negative samples were placed according to the actual working conditions and equipment operation design, and the test results were consistent with expectations, with no cross-contamination in the testing system. 5) Throughput and stress testing: Each system completed the individual donor-nucleic acid amplification testing (ID-NAT) of 276 samples in three batches within 12 hours, and successfully completed the ID-NAT test of 828 samples in three consecutive days. 6) Verification of reagent stability: After extreme storage (unsealed storage for 1 week with 4 freeze-thaw cycles), the reagents maintained 100% detection rate in the weak positive samples of HIV-1, HCV, and HBV, showing no significant differences from the control group (Kappa=1). 7) Verification of inter-system performance consistency: The system has stable operation performance, and the performance comparison results across the four devices were consistent (Kappa=1). 8) Process control measures: WanTag-Vortex Plus system software accurately controlled the equipment operation process with strict quality control measures, and correctly interpreted and safely reported the test results. Conclusion: The analytical and operational performance of the WanTag-Vortex Plus system complies with manufacturer design standards and essential laboratory workflow requirements. Integrated with laboratory information system (LIS), the system's control software meets standard process control requirements, yet requires further improvement.