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.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

ObjectiveTo investigate the effects of three-dimensional foot-ankle exercises (3DFAE) on mild-to-moderate hallux valgus in young women. MethodsFrom October to December, 2024, 45 female patients aged 18 to 26 years with hallux valgus were recruited from Capital University of Physical Education and Sports. They were randomly divided into control group (n = 15), toe-spread-out (TSO) group (n = 15) and 3DFAE group (n = 15). The control group only received foot health education, while the two intervention groups performed corresponding exercise training, for four weeks. Hallux valgus angle (HVA) was measured with photographic method, abductor hallucis activation was detected with surface electromyography, cross-sectional area of abductor hallucis was measured with Doppler ultrasound, and active ankle dorsiflexion range of motion (ROM) was assessed with a goniometer, before intervention, immediately after intervention, and four weeks after training cessation. A perceived difficulty scale was adopted to evaluate the training difficulty of TSO group and 3DFAE group at the first training session and upon completion of intervention. ResultsThe effects of time and interaction were significant for bilateral HVA, abductor hallucis activation, cross-sectional area of abductor hallucis and ankle dorsiflexion ROM (F > 2.728, P < 0.05), and the effect of group of left ankle dorsiflexion ROM was also significant (F = 4.995, P < 0.05). After intervention, bilateral HVA, abductor hallucis activation, cross-sectional area of abductor hallucis and ankle dorsiflexion ROM were all better than those before intervention (P < 0.05), and most indicators were superior to those measured at the 4-week follow-up (P < 0.05). The left ankle dorsiflexion ROM was better in 3DFAE group than in the control group and TSO group (P < 0.05). At the first intervention session, the perceived training difficulty was lower in 3DFAE group than in TSO group (t = 3.334, P = 0.002), and it decreased (t = 5.292, P < 0.001) after four weeks of training in TSO group, with no significant difference compared with 3DFAE group (P > 0.05). Conclusion3DFAE can improve ankle dorsiflexion ROM in young female patients with hallux valgus, with no more difficulty than TSO, and lower initial operation difficulty.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

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*

OBJECTIVES: To explore the factors related to prognosis and treatment failure in children with acute lymphoblastic leukemia (ALL). METHODS: A retrospective study was conducted to collect and analyze clinical data of ALL children admitted to the Department of Pediatric Hematology at Tongji Hospital, Huazhong University of Science and Technology, from January 2012 to December 2019, with follow-up until June 2024. RESULTS: A total of 341 children with ALL were included. Among the 69 children with treatment failure, 55 (80%) experienced relapse, while 14 (20%) had non-relapse-related deaths, and no secondary tumors were observed. Initial WBC count ≥50×10⁹/L, positive minimal residual disease, and severe adverse events were identified as independent risk factors for treatment failure (P<0.05). Among the 55 relapsed patients, early relapses were predominant (36%), and the primary site of relapse was the bone marrow (56%). Immunophenotyping (P=0.009), initial WBC count (P=0.011), and fusion genes (P=0.040) were associated with the timing of relapse. High-risk status, T-cell ALL, relapse, and severe adverse events were independent risk factors affecting long-term survival (P<0.05). CONCLUSIONS The prognosis of children with ALL is related to risk stratification, immunophenotyping, relapse status, and occurrence of severe adverse events. Among these factors, relapse is the primary cause of treatment failure. Actively preventing relapse may reduce the treatment failure rate and improve long-term survival.
Humans ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/therapy* ; Male ; Female ; Child ; Child, Preschool ; Retrospective Studies ; Prognosis ; Treatment Failure ; Adolescent ; Infant ; Risk Factors