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.

In the strategic context of "healthy China"， the concept of "medicine and food homology"， rooted in the culture of traditional Chinese medicine （TCM）， has received unprecedented attention. In response to population aging in China， the health of the elderly has become the focus of public health attention， and proactive health is the key to healthy aging. From the perspective of the application of medicinal and edible materials in proactive health and technological responses to population aging for the first time， this paper firstly provided a systematic overview of medicinal and edible materials and the policies related to proactive health. Second， it summarized the situation of modern technology that accelerates the research and development of medicinal and edible products， as well as the current situation of various modern biotechnologies that reveal the mechanism of action of medicinal and edible materials. Third， it discussed the application scenarios of medicinal and edible materials in proactive health and technological responses to population aging， as well as the future research and development of medicinal and edible materials. By exploring in depth the unique value and importance of medicinal and edible materials， the paper lays a theoretical foundation for improving the health care capabilities of TCM and contributes new strategies derived from TCM to healthy aging.

Osteoarthritis (OA), the most prevalent joint disease of late life, is closely linked to cellular senescence. Previously, we found that the senescence of fibroblast-like synoviocytes (FLS) played an essential role in the degradation of cartilage. In this work, single-cell sequencing data further demonstrated that cartilage acidic protein 1 (CRTAC1) is a critical secreted factor of senescent FLS, which suppresses mitophagy and induces mitochondrial dysfunction by regulating SIRT3 expression. In vivo, deletion of SIRT3 in chondrocytes accelerated cartilage degradation and aggravated the progression of OA. Oppositely, intra-articular injection of adeno-associated virus expressing SIRT3 effectively alleviated OA progression in mice. Mechanistically, we demonstrated that elevated CRTAC1 could bind with NRF2 in chondrocytes, which subsequently suppresses the transcription of SIRT3 in vitro. In addition, SIRT3 reduction could promote the acetylation of FOXO3a and result in mitochondrial dysfunction, which finally contributes to the degradation of chondrocytes. To conclude, this work revealed the critical role and underlying mechanism of senescent FLSs-derived CRTAC1 in OA progression, which provided a potential strategy for the OA therapy.