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 effects of Shuanglu Tongnao Formula on neuroinflammation in ischemic stroke (IS) rats via the P2X purinoceptor 7 receptor (P2X7R)/NLR family pyrin domain-containing 3 (NLRP3) pathway. Methods The rats were divided into five groups: the IS group, control group, Shuanglu Tongnao Formula group, P2X7R inhibitor brilliant blue G (BBG) group, and Shuanglu Tongnao Formula combined with P2X7R activator adenosine triphosphate (ATP) group, with 18 rats in each group. Except for the control group, rats in all other groups were used to construct an IS model using the suture method. After successful modeling, the drug was given once a day for 2 weeks. Neurological function scores and cerebral infarction volume ratios were measured in rats. Pathological examination of the ischemic penumbra brain tissue was performed. Immunofluorescence staining was used to quantify the proportions of microglia co-expressing both inducible nitric oxide synthase (iNOS) and ionized calcium-binding adapter molecule 1 (Iba1), as well as arginase 1 (Arg1) and Iba1, in the ischemic penumbra brain tissue. ELISA was used to detect tumor necrosis factor-alpha (TNF-α), transforming growth factor-beta (TGF-β), interleukin 6 (IL-6) and IL-10 in the ischemic penumbra brain tissue. Western blotting was used to measure P2X7R, NLRP3, and IL-1β proteins in the ischemic penumbra brain tissue. Results Compared with the control group, the IS group showed disordered neuronal arrangement, nuclear condensation, and obvious infiltration of inflammatory cells in the ischemic penumbra; significantly elevated neurological function scores, cerebral infarction volume ratios, proportions of microglia co-expressing iNOS and Iba1, and levels of TNF-α, IL-6, and P2X7R, NLRP3, IL-1β proteins; along with reduced proportions of microglia co-expressing Arg1 and Iba1 and levels of TGF-β and IL-10. Compared with the IS group, the Zhuang medicine Shuanglu Tongnao Formula and BBG groups demonstrated alleviated brain tissue damage; reduced neurological function scores, cerebral infarction volume ratios, proportions of microglia co-expressing iNOS and Iba1, and levels of TNF-α, IL-6, and P2X7R, NLRP3, IL-1β proteins; along with increased proportions of microglia co-expressing Arg1 and Iba1 and levels of TGF-β and IL-10. ATP reversed the effects of Zhuang medicine Shuanglu Tongnao Formula on microglial polarization and neuroinflammation in IS rats. Conclusion Zhuang medicine Shuanglu Tongnao Formula may promote the transformation of microglia from M1 type to M2 type by inhibiting the P2X7R/NLRP3 pathway, thereby improving neuroinflammation in IS rats.
Animals ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Receptors, Purinergic P2X7/metabolism* ; Male ; Drugs, Chinese Herbal/pharmacology* ; Rats ; Ischemic Stroke/pathology* ; Rats, Sprague-Dawley ; Disease Models, Animal ; Signal Transduction/drug effects* ; Neuroinflammatory Diseases/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Nitric Oxide Synthase Type II/metabolism* ; Interleukin-10/metabolism* ; Brain Ischemia/drug therapy* ; Microglia/metabolism*

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 explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.

Objective To analyze the medication rules and clinical experience of Qin Zujie in treating virtual labor based on data mining.Methods Outpatient medical records of Qin Zujie,diagnosed with deficiency syndrome at Ethnic Medicine Characteristic Diagnosis and Treatment Center,Guangxi International Zhuang Medical Hospital from January to December 2024 were selected.A standardized database was established and processed,yielding 148 clinical prescriptions containing 171 herbal ingredients.Frequency analysis,association rule analysis,and cluster analysis were employed to examine medication rules and clinical experience.Results The 20 Chinese herbal ingredients used more than 30 times each,with the first three being Zhigancao,Baizhu,Danggui.These herbs are primarily warm in nature,neutral in temperature,and slightly cold in effect,characterized by sweet,pungent,and bitter flavors.They mainly affect the spleen meridian and function to tonify deficiency,with Qi-tonifying herbs being the most common.Through association rule analysis,the most frequently combined pairs were Baizhu-Dangshen.Cluster analysis identified three core prescription clusters.Conclusion Qin Zujie's academic thought of treating virtual labor is to supplement deficiency as the main,detoxification as the auxiliary,and treat both the symptoms and the causes.

Objective:To summarize the characteristics and treatment strategies of tissue expander infections in auricular reconstruction using tissue expansion, providing references for the prevention and treatment of expander infections.Methods:A retrospective analysis was conducted on the data of patients who underwent auricular reconstruction using tissue expansion from January 2018 to January 2024 in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences. Patients meeting the inclusion criteria were included in the study. The causes of expander infections were summarized. Infections were categorized based on time periods (perioperative, inflation period, skin expansion period) and severity (mild, severe). The management and healing outcomes of different types of expander infections were recorded and their incidence rates were calculated. Descriptive statistical method were employed for analysis.Results:A total of 39 patients were included, with 25 males (64.1%) and 14 females (35.9%). The age was (8.2 ±1.9) years (range 5-13 years). Regarding infection causes, folliculitis of the expanded flap was noted in 9 cases (23.1%), inflation procedures in 10 cases (25.6%), insect bites in 2 cases (5.1%), and no obvious cause in 18 cases (46.2%). Perioperative infections occurred in 3 cases (7.7%), inflation period infections in 30 cases (76.9%), and skin expansion period infections in 6 cases (15.4%). Mild infections were present in 21 cases (53.8%) and severe infections in 18 cases (46.2%). After successful treatment of expander infections, 34 patients (87.2%) completed the second stage of reconstruction, while the remaining 5 cases(12.8%) had the expander removed and received ear reconstruction six months later.Conclusion:Infections of expanders during auricular reconstruction using tissue expansion are more common during the inflation period. Early management of potential causes of expander infections can reduce the risk of infection.

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.

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.

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.