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 systematically analyze the spatiotemporal distribution characteristics and epidemiological trends of tracheal, bronchus, and lung cancer (TBL) disease burden attributed to air pollution globally and in China and the United States from 1990 to 2021, and to assess the patterns of disease burden changes from 2022 to 2031 based on predictive models, providing a scientific basis for formulating targeted TBL prevention and control strategies. Methods Based on the Global Burden of Disease (GBD) 2021 database, we analyzed the disease burden data of TBL attributed to air pollution globally and in China and the United States from 1990 to 2021. R Studio 4.3.2 software was used to analyze the corresponding trends and the Bayesian age-period-cohort (BAPC) prediction model was used to predict the status of the disease burden of TBL attributed to air pollution in the world and in China and the United States from 2022 to 2031. Results In 2021, China had the highest number of deaths and disability-adjusted life years attributed to air pollution (211 400 patients and 4.8947 million person-years), followed by the United States (6 000 patients and 124 300 person-years). The age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) of TBL due to air pollution in the world and in China and the United States showed a decreasing trend. From 1990 to 2021, the ASMR and ASDR of TBL in China due to air pollution were much higher than those in the United States and the global average. In terms of gender, from 1990 to 2021, the disease burden of male patients with TBL attributed to air pollution was much higher than that of female patients. The BAPC prediction model showed that from 2022 to 2031, the ASMR and ASDR of TBL attributed to air pollution showed an upward trend globally, while they showed a downward trend in China and the United States. Conclusion Over the past 30 years, the air pollution-related TBL disease burden in the world and in China and the United States has continued to decline, but China's disease burden is still significantly higher than the global average. The disease burden in men far exceeds that in women, with men and the population aged ≥50 years being high-risk groups. In the future, the global disease trend may reverse and rise, while China and the United States are expected to continuously decline. However, precise prevention and control for high-risk groups remains a key challenge.

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 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:Previous studies have demonstrated that acupuncture at the governor meridian has precise efficacy in the treatment of ischemic stroke and can improve cerebral ischemia-reperfusion injury by attenuating pyroptosis,but the upstream regulatory mechanisms are not yet fully clarified.OBJECTIVE:To observe the neuroprotective effect of electroacupuncture in model rats of cerebral ischemia-reperfusion injury.METHODS:Twenty-seven Sprague-Dawley rats were randomly divided into sham surgery,model,and electroacupuncture groups,with nine rats in each group.Modified suture method was used to establish cerebral ischemia-reperfusion model rats in the model and electroacupuncture groups.The electroacupuncture group was subjected to electroacupuncture at"Baihui,""Fengfu,"and"Dazhui"acupoints,20 minutes each,once a day,for 7 consecutive days.After treatment,neurological deficit scoring and pole test were performed to assess behavioral changes.Tri-phenyl tetrazolium chloride staining was used to assess cerebral infarction size in rats.Hematoxylin-eosin staining was performed to observe morphological changes in cerebral cortex tissue on the infarcted side of rats.Immunofluorescence analysis was used to determine Iba-1 and reactive oxygen species levels in cerebral cortex tissue on the infarcted side of rats,ELISA method was used for measuring interleukin-1β,interleukin-6 and tumor necrosis factor α levels in cerebral cortex tissue on the infarcted side of rats.Real-time fluorescence quantitative PCR and western blot were used to detect mRNA and protein expression levels of thioredoxin interaction protein,nod-like receptor associated protein 3(NLRP3),Caspase-1 and interleukin-1β in cerebral cortex tissue on the infarcted side of rats respectively,and the interaction between thioredoxin interaction protein and NLRP3 was analyzed by immunoprecipitation.RESULTS AND CONCLUSION:(1)Compared with the sham surgery group,rats in the model group showed an increase in neurological deficit score,pole test score,cerebral infarction volume(P<0.05),the immunofluorescence expression of Iba-1 and reactive oxygen species(P<0.05),the levels of interleukin-1β,interleukin-6 and tumor necrosis factor α(P<0.05),and the mRNA and protein expression of thioredoxin interaction protein,NLRP3,Caspase-1 and interleukin-1β in cerebral cortex tissue(P<0.05).Hematoxylin-eosin staining in the model group showed neuronal degeneration and necrosis,with fragmented and dissolved nuclei and cellular vacuoles.(2)Compared with the model group,rats in the electroacupuncture group showed a reduction in neurological deficit score,pole climbing test score,cerebral infarction volume(P<0.05),the immunofluorescence expression of Iba-1 and reactive oxygen species(P<0.05),the levels of interleukin-1β,interleukin-6 and tumor necrosis factor α(P<0.05),and the mRNA and protein expression of thioredoxin interaction protein,NLRP3,Caspase-1 and interleukin-1β in cerebral cortex tissue(P<0.05).Hematoxylin-eosin staining showed that the pathological damage of neurons in cerebral cortex tissue on the infarcted side of rats in the electroacupuncture group was significantly attenuated,with significantly reduced cell necrosis and vacuolation.(3)Immunoprecipitation assay showed an interaction between thioredoxin interaction proteins and NLRP3 in the cerebral cortical tissues on the infarcted side of rats in the model group.To conclude,electroacupuncture has a significant therapeutic effect against cerebral ischemia-reperfusion injury,possibly by inhibiting the reactive oxygen species/thioredoxin interaction protein/NLRP3 cell pyroptosis signaling pathway and activation of microglia to reduce the release of inflammatory factors.

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.

BACKGROUND:Previous studies have demonstrated that acupuncture at the governor meridian has precise efficacy in the treatment of ischemic stroke and can improve cerebral ischemia-reperfusion injury by attenuating pyroptosis,but the upstream regulatory mechanisms are not yet fully clarified.OBJECTIVE:To observe the neuroprotective effect of electroacupuncture in model rats of cerebral ischemia-reperfusion injury.METHODS:Twenty-seven Sprague-Dawley rats were randomly divided into sham surgery,model,and electroacupuncture groups,with nine rats in each group.Modified suture method was used to establish cerebral ischemia-reperfusion model rats in the model and electroacupuncture groups.The electroacupuncture group was subjected to electroacupuncture at"Baihui,""Fengfu,"and"Dazhui"acupoints,20 minutes each,once a day,for 7 consecutive days.After treatment,neurological deficit scoring and pole test were performed to assess behavioral changes.Tri-phenyl tetrazolium chloride staining was used to assess cerebral infarction size in rats.Hematoxylin-eosin staining was performed to observe morphological changes in cerebral cortex tissue on the infarcted side of rats.Immunofluorescence analysis was used to determine Iba-1 and reactive oxygen species levels in cerebral cortex tissue on the infarcted side of rats,ELISA method was used for measuring interleukin-1β,interleukin-6 and tumor necrosis factor α levels in cerebral cortex tissue on the infarcted side of rats.Real-time fluorescence quantitative PCR and western blot were used to detect mRNA and protein expression levels of thioredoxin interaction protein,nod-like receptor associated protein 3(NLRP3),Caspase-1 and interleukin-1β in cerebral cortex tissue on the infarcted side of rats respectively,and the interaction between thioredoxin interaction protein and NLRP3 was analyzed by immunoprecipitation.RESULTS AND CONCLUSION:(1)Compared with the sham surgery group,rats in the model group showed an increase in neurological deficit score,pole test score,cerebral infarction volume(P<0.05),the immunofluorescence expression of Iba-1 and reactive oxygen species(P<0.05),the levels of interleukin-1β,interleukin-6 and tumor necrosis factor α(P<0.05),and the mRNA and protein expression of thioredoxin interaction protein,NLRP3,Caspase-1 and interleukin-1β in cerebral cortex tissue(P<0.05).Hematoxylin-eosin staining in the model group showed neuronal degeneration and necrosis,with fragmented and dissolved nuclei and cellular vacuoles.(2)Compared with the model group,rats in the electroacupuncture group showed a reduction in neurological deficit score,pole climbing test score,cerebral infarction volume(P<0.05),the immunofluorescence expression of Iba-1 and reactive oxygen species(P<0.05),the levels of interleukin-1β,interleukin-6 and tumor necrosis factor α(P<0.05),and the mRNA and protein expression of thioredoxin interaction protein,NLRP3,Caspase-1 and interleukin-1β in cerebral cortex tissue(P<0.05).Hematoxylin-eosin staining showed that the pathological damage of neurons in cerebral cortex tissue on the infarcted side of rats in the electroacupuncture group was significantly attenuated,with significantly reduced cell necrosis and vacuolation.(3)Immunoprecipitation assay showed an interaction between thioredoxin interaction proteins and NLRP3 in the cerebral cortical tissues on the infarcted side of rats in the model group.To conclude,electroacupuncture has a significant therapeutic effect against cerebral ischemia-reperfusion injury,possibly by inhibiting the reactive oxygen species/thioredoxin interaction protein/NLRP3 cell pyroptosis signaling pathway and activation of microglia to reduce the release of inflammatory factors.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.

Magnetic resonance angiography(MRA)is a non-invasive imaging technique used to observe blood vessels.Quantitative analysis of MRA images enables visualization of vascular pathways,condition,and blood flow dynamics,which is essential for diagnosing vascular diseases such as vascular lesions,stenosis,and occlusions.Vessel segmentation serves as the fundamental basis for quantitative vascular analysis.However,the complex morphology of vessels,difficulties in labeling,and scarcity of accurate 3D vascular annotations pose significant challenges for MRA-based vessel segmentation.A strategy of selectively occluding vessels during model training is proposed to enhance the algorithm's capacity to capture the topological structure of blood vessels,thereby improving the continuity of vessel segmentation results.Additionally,a Refine network is incorporated to refine the binary segmentation results of the segmentation network,thereby further improving segmentation accuracy.Model training and testing are carried out using 42 cases of 3D MRA data from the public MIDAS dataset.For the test set,the 3D U-Net baseline model with vessel occlusion strategy shows a β0 Error of 1.2742±0.2103 and a β1 Error of 0.3393±0.0818,respectively,which are 0.1136 and 0.0280 lower than the baseline.The model integrating vessel occlusion strategy and Refine network achieves an average Dice score of 0.7105±0.0125,which is 0.0028 higher than the baseline.These results demonstrate that the proposed method effectively improves both vascular connectivity and segmentation accuracy.