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.

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 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.

Objective: To analyze the epidemiological characteristics and incidence trends of pulmonary tuberculosis (TB) in children aged 0-14 years in Shaanxi Province from 2010 to 2024, so as to provide a reference for optimizing child TB prevention and control strategies. Methods: Data on pulmonary TB cases in children aged 0-14 years and demographic information in Shaanxi Province from 2010 to 2024 were collected from Surveillance and Reporting Management System with Disease Prevention and Control Information Management System under the National Health Security Informatization Project Disease Prevention and Control Information System. A Joinpoint regression model was established to analyze the temporal, spatial, and population distribution trends of child pulmonary TB incidence. Results: A total of 2 954 cases of pulmonary TB in children aged 0-14 years were reported in Shaanxi Province from 2010 to 2024, accounting for 0.97% of all TB cases in the general population. The average annual reported incidence rate in children was 3.32 per 100 000. Among these cases, 804 were pathogenetically positive, showing a increasing trend ( χ 2 trend =420.94, P < 0.01 ). The overall reported incidence rate of pulmonary TB in children aged 0-14 years in Shaanxi Province showed a decreasing trend, dropping from 5.35 per 100 000 in 2010 to 2.41 per 100 000 in 2024. Joinpoint regression analysis identified three distinct phases for the reported incidence rate of TB:a rapid decline from 2010 to 2013 (APC=-20.02%, 95% CI = -33.64% to -10.42%), a slight increase from 2013 to 2017 (APC=11.18%, 95% CI =3.07%-24.17%) and a slight decline again from 2017 to 2024 (APC= -7.27 %, 95% CI =-12.73% to -4.30%) (all P <0.01). Among children aged 0-14 years, the age group with the highest average annual reported incidence rate was 10-14 years (8.02 per 100 000), followed by 5-9 years (1.44 per 100 000), and 0-4 years had the lowest rate (0.95 per 100 000). The difference in reported incidence rates among the three age groups was statistically significant ( χ 2= 51.91, P <0.01). The average annual reported incidence rate of TB was 3.25 per 100 000 in boys and 3.39 per 100 000 in girls, with no statistically significant difference ( χ 2=2.01, P >0.05). There was no obvious periodic variation in the annual case reporting. Among all cities in Shaanxi Province, Ankang City had the highest average annual reported incidence rate (5.16 per 100 000). Conclusions From 2010 to 2024, the reported incidence rate of pulmonary TB in children aged 0-14 years in Shaanxi Province showed an overall decreasing trend. However, it is still necessary to strengthen active surveillance, implement targeted measures in high incidence areas such as Ankang City, and maintain continuous attention to child TB prevention and control.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

Objective　To analyze the epidemiological characteristics and infection pathways and virulence factors of one cholera outbreak in Fengxian District, Shanghai, China, in 2024, and to provide scientific basis for epidemic control and prevention. Methods　Epidemiologic data of cases of one cholera outbreak in Fengxian District in 2024 were collected using on-site epidemiologic survey methods; RT-PCR nucleic acid testing and bacterial culture were applied to carry out pathogenicity testing of cases, close contacts, environment, and food samples; and the genome sequences of the strains were obtained using second-generation gene sequencing. Results　The case was a 62-year-old woman, who presented to the doctor with diarrhea for 4 consecutive days, 4-5 times a day, with watery stools, which was not effectively relieved by self-administered medication. There was no history of traveling away from Shanghai for 5 days before the onset of the disease, and she was engaged in the preparation and delivery of food for rural banquets during the period. Vibrio cholerae O139 was detected in the anal swab sample of the case and the septic tank of the workplace on the 4th day after the onset of the disease; samples of turtle and links in the store selling turtle were cultured for Vibrio cholerae O139. The isolate carried several virulence-related genes such as ctxA, ctxB, HlyA, zot, rtxA, hapA, nanH, tdh, and T3SS. Comparison of the isolate with the O139 strain of cholera cluster within our country through the National Pathogenic Bacteria Recognition Network (NPBN) in recent years suggests that the closest environmental or aquatic animal isolate to the sequence of this strain is the turtle isolate uploaded at a place in Guangdong. Conclusion　This outbreak was a disseminated outbreak caused by the case's contact with turtle contaminated with Vibrio cholerae O139, and early detection of enteric infectious diseases such as cholera can be achieved by relying on the outpatient enteric cholera surveillance network.

Objective To measure radiation filed distribution in the treatment room of the Varian Halcyon medical linear accelerator, and to provide a basis for shielding design and potential exposure analysis of treatment rooms for this type of accelerator. Methods Under the 6 MV X-ray (FFF) mode at a maximum dose rate of 800 MU/min and a maximum irradiation field of 28.00 cm × 28.00 cm, a total of 540 MU was delivered during gantry rotation. Radiation field distribution was measured using thermoluminescence dosimeters located at multiple points in the room. The measured data were then applied to shielding calculations, and the results were compared with those obtained using empirical formulas. Results The overall radiation levels in the treatment room were in the range of 12.2 µGy/540 MU to 5.520 Gy/540 MU, with the highest dose (5.520 Gy/540 MU) observed at the isocenter, and the lowest dose (12.2 µGy/540 MU) recorded at approximately 6.5 m from the gantry head. The radiation levels at most points were within the range of 100-1000 µGy/540 MU. At heights of 0.5, 1.1, and 1.7 m, the radiation field exhibited a rapid attenuation from the beam center outward, with a faster decay along the treatment couch direction (Y-axis) than along the perpendicular direction (X-axis). Shielding calculations based on the measured radiation field yielded required wall thicknesses of 1219 mm (east wall), 949 mm (south wall), 1235 mm (west wall), and 1252 mm (north wall), all of which were smaller than those calculated using empirical formulas. Conclusion Thermoluminescence dosimeter is suitable for multi-point in situ measurement of radiation field distribution in Halcyon accelerator treatment rooms. The measurement results can be used to optimize the shielding design of Halcyon accelerator treatment rooms.