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 changes and correlations of color， components and anti-tumor activity of Gelsemium elegans during sand-frying processing， so as to provide a reference for the toxicity-attenuating and efficacy-preserving processing as well as quality control of sand-fried G. elegans decoction pieces.METHODS Raw G.elegans and its processed products prepared by sand-frying for 15， 30， 75， 105 and 120 s were taken as test samples. The chromatic values， contents of total alkaloids and seven main active components （gelsemine， humantenine， koumine， gelsenicine， etc.） were determined. Their in vitro anti-tumor activity was investigated. The correlations between color and component contents， as well as between total alkaloid content and anti-tumor activity were analyzed.RESULTS The color of G.elegans samples generally darkened after sand-frying， and the total color difference （Δ E * ） of samples sand-fried for 75 s was greater than 20. At the initial stage of sand-frying （15-30 s）， the total alkaloid content in processed products decreased sharply compared with the raw material； at the later-stage of sand-frying （75-105 s）， the declining rate of total alkaloid content slowed down and tended to be stable. The contents of gelsenicine and humantenidine in processed products dropped drastically at 30 s of sand-frying， while the contents of koumine and sempervirine decreased substantially （by about 80%） after 75 s of sand-frying and then gradually tended to be stable. The anti-HepG2 cell proliferation activity of each processed product decreased with the extension of frying time. Correlation analysis demonstrated that color brightness and yellow-blue chromaticity were significantly positively correlated with the contents of total alkaloids and all determined active components except gelsemine （ P ＜0.05 or P ＜0.01）； red-green chromaticity was significantly positively correlated with the contents of koumine， sempervirine， gelsemivirine and humantenidine （ P ＜0.05 or P ＜0.01）； Δ E * was significantly negatively correlated with the contents of total alkaloids and all determined active components except gelsemine. Besides， total alkaloid content was significantly negatively correlated with the half maximal inhibitory concentration of the samples on HepG2 cells （ P ＜0.05 or P ＜0.01）.CONCLUSIONS With the prolongation of sand-frying time， the color of sand-fried G.elegans decoction pieces gradually turned brownish-brown， the contents of seven active components and total alkaloids decreased progressively， and its anti-HepG2 cell activity showed an overall downward trend. Sand-frying for 75 s serves as a critical turning-point for the above-mentioned changes.

Objective To explore the application effect of prone position ventilation combined with veno-venous extracorporeal membrane oxygenation (VV-ECMO) in the treatment of severe primary graft dysfunction (PGD) after lung transplantation. Methods The clinical data of 75 lung transplant recipients who developed severe PGD after lung transplantation and were treated with VV-ECMO from January 2021 to June 2024 at Wuxi People's Hospital Affiliated to Nanjing Medical University were collected. The patients with severe graft dysfunction after lung transplantation were divided into VV-ECMO group (control group, 45 cases) and prone position ventilation combined with VV-ECMO group (treatment group, 30 cases). The general data of the two groups of patients were compared, including the donors' clinical data (age, gender and oxygenation index, etc) and the recipients' clinical data [gender, age and body mass index (BMI), etc]. Cox regression analysis was used to analyze the influencing factors of the recipients' 30-day, 90-day and 180-day survival after surgery. The survival curves of the two groups of recipients were drawn using Kaplan-Meier method and compared using the log-rank test. Results The intensive care unit (ICU) stay time, ECMO application time and ventilator use time of control group were longer than those of treatment group. The proportion of male recipients and the BMI of control group were lower than those of treatment group. The 30-day, 90-day and 180-day survival of control group was worse than that of treatment group, and the differences were statistically significant (all P<0.05). The univariate Cox regression analysis of the recipients' 30-day survival after surgery showed that the recipients' BMI, history of diabetes, enlargement of the right atrium and right ventricle, intraoperative blood transfusion volume and intraoperative red blood cell transfusion volume were statistically significant (all P<0.05). The multivariate Cox regression analysis showed that the history of diabetes and enlargement of the right atrium and right ventricle were risk factors affecting the 30-day survival of lung transplant recipients (all P<0.05). The univariate Cox regression analysis of the recipients' 90-day survival after surgery showed that the recipients' BMI, history of diabetes, enlargement of the right atrium and right ventricle, intraoperative blood transfusion volume, intraoperative red blood cell transfusion volume and group variable were statistically significant (all P<0.05). The multivariate Cox regression analysis showed that the history of diabetes, enlargement of the right atrium and right ventricle and group variable were risk factors affecting the 90-day survival of lung transplant recipients (all P<0.05). The univariate Cox regression analysis of the recipients' 180-day survival after surgery showed that the recipients' BMI, history of diabetes, right atrium and right ventricle enlargement, intraoperative blood transfusion volume, intraoperative red blood cell transfusion volume and group variable were statistically significant (all P<0.05). The multivariate Cox regression analysis showed that the history of diabetes, enlargement of the right atrium and right ventricle and group variable were risk factors affecting the 180-day survival of lung transplant recipients (all P<0.05). The 30-day, 90-day and 180-day survival rates of control group were lower, and the differences between the two groups were statistically significant (all P<0.05), with a median survival time of 100 days in control group. Conclusions In the clinical treatment of severe PGD after lung transplantation, prone position ventilation combined with VV-ECMO may shorten ECMO application time, invasive ventilation time and ICU stay time, and improve the short-term prognosis of lung transplantation.

To compare gait parameters during single-task and dual-task walking in older adults, and to examine differences in dual-task costs between individuals with high versus low balance abilities under different task conditions.

To explore the dose-response correlation between appendicular skeletal muscle index (ASMI) and muscle strength in older adults.

OBJECTIVE To investigate the analgesic effect and potential mechanism of punicalagin on neuropathic pain （NP） rats based on the hypoxia-inducible factor-1α （HIF-1α）/nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 （NLRP3） signaling pathway. METHODS Male SD rats were randomly divided into sham operation group （18 rats） and modeling group （72 rats）. NP rat model was established by chronic constriction injury （CCI） of sciatic nerve. The successfully modeled rats were divided into NP group， 2-methoxyestradiol group （HIF-1α antagonist 10 mg/kg）， punicalagin group （300 mg/kg）， and punicalagin+dimethyloxaloglycine group （punicalagin 300 mg/kg+HIF-1α agonist 175 mg/kg）， with 18 rats in each group. Rats in each group were injected intraperitoneally and/or intragastrically with the corresponding solution or 1% dimethyl sulfoxide/normal saline， once a day， for 14 consecutive days. After the last administration， the mechanical withdrawal threshold （MWT）， thermal withdrawal latency （TWL）， the levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β） and IL-6 in spinal cord tissue were detected； the morphological changes in the spinal dorsal horn were observed. Apoptosis rate of spinal dorsal horn neurons， the co-localization of NLRP3/ionized calcium binding adapter molecule 1 （Iba-1） （calculated by the number of NLRP3+/Iba-1+ cells） and the protein expressions of HIF-1α， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC） and caspase-1 in spinal cord tissue were detected. RESULTS Compared with the sham operation group， neurofibril in spinal dorsal horn of rats in NP group was thickened and wound into knots， and vacuolar degeneration containing silver granules was observed； the MWT and TWL were reduced or shortened； the levels of TNF-α， IL-1β and IL-6 in spinal cord tissue， the apoptosis rate of spinal dorsal horn neurons， the number of NLRP3+/Iba-1+ cells， and protein expressions of HIF-1α， NLRP3， ASC and caspase-1 were significantly increased or up-regulated （P＜0.05）. Compared with the NP group， the above indexes were significantly improved in the 2-methoxyestradiol group and punicalagin group （P＜0.05）， while dimethyloxaloglycine could significantly reverse the improvement effect of punicalagin on the above indexes （P＜0.05）. CONCLUSIONS Punicalagin can relieve pain in NP rats， and its analgesic effect may be achieved by inhibiting HIF-1α/NLRP3 signaling pathway and blocking the activation of ma0o4e@163.com NLRP3 inflammasome in spinal dorsal horn microglia.

BACKGROUND:Psoralen has a strong anti-osteoporotic activity and may have a restorative effect on chemotherapy-induced osteoporosis. OBJECTIVE:To explore the restorative effect of psoralen on the osteogenic differentiation of bone marrow mesenchymal stem cells in mice inhibited by cyclophosphamide and its mechanism. METHODS:C57BL/6 mouse bone marrow mesenchymal stem cells were isolated and cultured.Effect of psoralen on viability of bone marrow mesenchymal stem cells was detected by MTT assay.Osteogenic induction combined with alkaline phosphatase staining was used to determine the optimal dose of psoralen to restore the osteogenic differentiation of bone marrow mesenchymal stem cells inhibited by cyclophosphamide.The mRNA expression levels of Runx2,alkaline phosphatase,Osteocalcin,osteoprotegerin,and Wnt/β-catenin signaling pathway-related genes Wnt1,Wnt4,Wnt10b,β-catenin,and c-MYC were measured by RT-qPCR at different time points under the intervention with psoralen.The protein expression of osteogenic specific transcription factor Runx2 and Wnt/β-catenin signaling pathway related genes Active β-catenin,DKK1,c-MYC,and Cyclin D1 was determined by western blot assay at different time points under the intervention with psoralen. RESULTS AND CONCLUSION:(1)There was no significant effect of different concentrations of psoralen on the viability of bone marrow mesenchymal stem cells.The best recovery of the inhibition of osteogenic differentiation of bone marrow mesenchymal stem cells caused by cyclophosphamide was under the intervention of psoralen at a concentration of 200 μmol/L.(2)Psoralen reversed the reduction in osteogenic differentiation marker genes Runx2,alkaline phosphatase,Osteocalcin and osteoprotegerin mRNA expression and Runx2 protein expression in bone marrow mesenchymal stem cells caused by cyclophosphamide conditioned medium.(3)Psoralen reversed the decrease in Wnt/β-catenin pathway-related genes Wnt4,β-catenin,c-MYC mRNA and Active β-catenin,c-MYC,and Cyclin D1 protein expression and the increase in DKK1 protein expression in bone marrow mesenchymal stem cells caused by cyclophosphamide conditioned medium.(4)The results showed that cyclophosphamide inhibited osteogenic differentiation of bone marrow mesenchymal stem cells in mice,and psoralen had a restorative effect on it.The best intervention effect was achieved at a concentration of 200 μmol/L psoralen,and this protective effect might be related to the activation of Wnt4/β-catenin signaling pathway by psoralen.

ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

Objective To evaluate the current radiation doses in pediatric non-cardiac interventional procedures, and analyze the associated clinical factors, and to provide data references for reducing pediatric radiation exposure. Methods We conducted a retrospective analysis of the radiation doses of children who had undergone non-cardiac interventional procedures at the interventional department of a tertiary pediatric hospital in Jinan from January 2022 to October 2024. The collected data included basic demographic information, surgical date, anatomical site, disease type, and radiation dose parameters (cumulative fluoroscopy time, cumulative dose area product in cine mode, cumulative air kerma, and the number of images acquired). The Kruskal-Wallis H test was used for comparative analysis between groups (P < 0.05 was considered statistically significant). Results Among the 475 included children, 99 cases (20.8%) had infantile hemangioma (median P_ka, 0.136 Gy·cm²; median K_a,r, 0.38 mGy), 235 cases (49.5%) had venous malformation (median P_ka, 9.82 Gy·cm²; median K_a,r, 40.99 mGy), 75 cases (15.8%) had lymphatic malformation (median P_ka, 0.06 Gy·cm²; median K_a,r, 0.18 mGy), 32 cases (6.7%) had retinoblastoma (median P_ka, 6.58 Gy·cm²; median K_a,r, 52.34 mGy), 12 cases (2.5%) had arteriovenous malformation (median P_ka, 42.3 Gy·cm²; median K_a,r, 162.87 mGy), and 22 cases (4.6%) had other vascular malformations (median P_ka, 21.7 Gy·cm²; median K_a,r, 89.1 mGy). There were significant differences between children with different disease types in the cumulative fluoroscopy time, cumulative dose area product in cine mode, cumulative air kerma at the patient entrance reference point, and the number of images acquired during non-cardiac interventional procedures (all P < 0.01). Conclusion This study presented the types and proportions of pediatric non-cardiac interventional procedures, evaluated the radiation dose levels of different surgical types, and analyzed the effects of weight and anatomical site on radiation exposure, which can be useful for preliminary assessment of radiation doses in pediatric non-cardiac interventional procedures.