BACKGROUND:The sports medicine community has widely called for the use of machine learning technology to efficiently process the huge and complicated sports data resources,and construct intelligent sports injury prediction models,enabling accurate early warning of sports injuries.It is of great significance to comprehensively summarize and review such research results so as to grasp the direction of early warning model improvement and to guide the construction of sports injury prediction models in China. OBJECTIVE:To systematically review and analyze relevant research on sports injury prediction models based on machine learning technology,thereby providing references for the development of sports injury prediction models in China. METHODS:Literature search was conducted on CNKI,Web of Science and EBSCO databases,which mainly searched for literature related to machine learning techniques and sports injuries.Finally,61 articles related to sports injury prediction models were included for analysis. RESULTS AND CONCLUSION:(1)In terms of external risk feature indicators,there is a lack of competition scenario indicators,and the inclusion of related feature indicators needs to be further improved to further enrich the dimensions of the dataset for model training.In addition,the inclusion feature weighting methods of the sports injury prediction model are mainly based on filtering methods and the use of embedding and wrapping weighting methods needs to be strengthened in order to enhance the analysis of the interaction effects of multiple risk factors.(2)In terms of model body training,supervised learning algorithms become the mainstream choice.Such algorithms have higher requirements for the completeness of sample labeling information,and the application scenarios are easily limited.Therefore,the application of unsupervised and semi-supervised algorithms can be increased in the later stage.(3)In terms of model performance evaluation and optimization,the current studies mainly adopt two verification methods:HoldOut crossover and k-crossover.The range of AUC values is(0.76±0.12),the range of sensitivity is(75.92±11.03)%,the range of specificity is(0.03±4.54)%,the range of F1 score is(80.60±10.63)%,the range of accuracy is(69.96±13.10)%,and the range of precision is(70±14.71)%.Data augmentation and feature optimization are the most common model optimization operations.The accuracy and precision of the current sports injury prediction model are about 70%,and the early warning effect is good.However,the model optimization operation is relatively single,and data augmentation methods are often used to improve model performance.Further adjustments to the model algorithm and hyperparameters are needed to further improve model performance.(4)In terms of model feature extraction,most of the internal risk profile indicators included are mainly based on anthropometrics,training load,years of training,and injury history,but there is a lack of sports recovery and physical function indicators.

ObjectiveTo investigate the role of silent information regulatory protein （SIRT6）/hypoxia-inducible factor-1α （HIF-1α） pathway in regulating the reprogramming of glucose metabolism in ulcerative colitis （UC） and the mechanism of intervention of Shaoyaotang. MethodsForty-eight c57bL/6 mice were randomly divided into a blank group， a model group， a Mesalazine group （0.42 g·kg^-1）， a Shaoyaotang group （31.08 g·kg^-1）， an inhibitor group （OSS-128167， 50 mg·kg^-1）， and an inhibitor + Shaoyaotang group （50 mg·kg^-1 OSS-128167 + 31.08 g·kg^-1 Shaoyaotang）. A UC model was established by the administration of 2.5% dextran sulfate sodium （DSS） solution for mice in other groups for 7 d， except for the blank group. The mice in each group were treated with saline， Mesalazine， Shaoyaotang， inhibitor， and inhibitor + Shaoyaotang， respectively， for 7 d. The mice were necropsied 24 h after the last administration of the drug. The blood was collected from the orbital region， and colon tissue was taken. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in colon tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to detect serum interleukin （IL）-10， IL-17， and IL-6 levels. A biochemical method was used to detect glucose and lactate dehydrogenase A （LDHA） levels. Immunohistochemistry （IHC） was employed to detect IL-22 and transforming growth factor-β₁ （TGF-β₁） levels in colon tissue， and Western blot and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） were used to detect relative protein and mRNA expressions of SIRT6， HIF-1α， and LDHA. ResultsCompared with those of the blank group， disease activity index （DAI） scores of mice in the model group and inhibitor group were significantly increased （P<0.01）. The length of colon tissue was significantly shortened， and colon tissue was congested and eroded. The pathohistological scores were significantly increased （P<0.01）. The levels of serum inflammatory factors IL-17 and IL-6 were significantly elevated， and the levels of IL-10 were significantly decreased （P<0.01）. The protein expressions of IL-22 and TGF-β₁ were significantly reduced in colon tissue （P<0.01）. The relative protein and mRNA expressions of SIRT6 were significantly decreased （P<0.01）， and the relative protein and mRNA expressions of HIF-1α and LDHA and the contents of glucose and lactate were significantly elevated （P<0.01）. The level of inflammation in the colon of the mice in the inhibitor group was more severe than that in the model group （P<0.01）. Compared with the model group， the Mesalazine group， the Shaoyaotang group， and the inhibitor + Shaoyaotang group showed reduced colonic injury， significant decrease in serum IL-17 and IL-6， significant increase in IL-10 （P<0.01）， significant increase in the protein expressions of IL-22 and TGF-β₁ in colon tissue （P<0.01）， significant increase in the protein expressions of SIRT6 and the relative mRNA expressions （P<0.01）， and significant reduction in the protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate （P<0.01）. Compared with those in the Shaoyaotang group， the serum IL-17 and IL-6 were significantly increased， and IL-10 was significantly decreased in the inhibitor + Shaoyaotang group （P<0.01）. The protein expressions of IL-22 and TGF-β₁ in colon tissue were significantly decreased （P<0.01）. The expressions of SIRT6 protein and the relative mRNA expressions were significantly decreased （P<0.01）. The protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate were significantly elevated （P<0.01）. However， the difference between the Shaoyaotang group and the Mesalazine group was not significant. ConclusionShaoyaotang can effectively treat DSS-induced mice with UC through the SIRT6/HIF-1α pathway， and its mechanism of action may be related to the regulation of the SIRT6/HIF-1α pathway and glucose metabolism reprogramming and the inhibition of glycolysis.

ObjectiveTo investigate the role of silent information regulatory protein （SIRT6）/hypoxia-inducible factor-1α （HIF-1α） pathway in regulating the reprogramming of glucose metabolism in ulcerative colitis （UC） and the mechanism of intervention of Shaoyaotang. MethodsForty-eight c57bL/6 mice were randomly divided into a blank group， a model group， a Mesalazine group （0.42 g·kg^-1）， a Shaoyaotang group （31.08 g·kg^-1）， an inhibitor group （OSS-128167， 50 mg·kg^-1）， and an inhibitor + Shaoyaotang group （50 mg·kg^-1 OSS-128167 + 31.08 g·kg^-1 Shaoyaotang）. A UC model was established by the administration of 2.5% dextran sulfate sodium （DSS） solution for mice in other groups for 7 d， except for the blank group. The mice in each group were treated with saline， Mesalazine， Shaoyaotang， inhibitor， and inhibitor + Shaoyaotang， respectively， for 7 d. The mice were necropsied 24 h after the last administration of the drug. The blood was collected from the orbital region， and colon tissue was taken. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in colon tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to detect serum interleukin （IL）-10， IL-17， and IL-6 levels. A biochemical method was used to detect glucose and lactate dehydrogenase A （LDHA） levels. Immunohistochemistry （IHC） was employed to detect IL-22 and transforming growth factor-β₁ （TGF-β₁） levels in colon tissue， and Western blot and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） were used to detect relative protein and mRNA expressions of SIRT6， HIF-1α， and LDHA. ResultsCompared with those of the blank group， disease activity index （DAI） scores of mice in the model group and inhibitor group were significantly increased （P<0.01）. The length of colon tissue was significantly shortened， and colon tissue was congested and eroded. The pathohistological scores were significantly increased （P<0.01）. The levels of serum inflammatory factors IL-17 and IL-6 were significantly elevated， and the levels of IL-10 were significantly decreased （P<0.01）. The protein expressions of IL-22 and TGF-β₁ were significantly reduced in colon tissue （P<0.01）. The relative protein and mRNA expressions of SIRT6 were significantly decreased （P<0.01）， and the relative protein and mRNA expressions of HIF-1α and LDHA and the contents of glucose and lactate were significantly elevated （P<0.01）. The level of inflammation in the colon of the mice in the inhibitor group was more severe than that in the model group （P<0.01）. Compared with the model group， the Mesalazine group， the Shaoyaotang group， and the inhibitor + Shaoyaotang group showed reduced colonic injury， significant decrease in serum IL-17 and IL-6， significant increase in IL-10 （P<0.01）， significant increase in the protein expressions of IL-22 and TGF-β₁ in colon tissue （P<0.01）， significant increase in the protein expressions of SIRT6 and the relative mRNA expressions （P<0.01）， and significant reduction in the protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate （P<0.01）. Compared with those in the Shaoyaotang group， the serum IL-17 and IL-6 were significantly increased， and IL-10 was significantly decreased in the inhibitor + Shaoyaotang group （P<0.01）. The protein expressions of IL-22 and TGF-β₁ in colon tissue were significantly decreased （P<0.01）. The expressions of SIRT6 protein and the relative mRNA expressions were significantly decreased （P<0.01）. The protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate were significantly elevated （P<0.01）. However， the difference between the Shaoyaotang group and the Mesalazine group was not significant. ConclusionShaoyaotang can effectively treat DSS-induced mice with UC through the SIRT6/HIF-1α pathway， and its mechanism of action may be related to the regulation of the SIRT6/HIF-1α pathway and glucose metabolism reprogramming and the inhibition of glycolysis.

Due to its synergistic effects and reduced side effects, combination therapy has become an important strategy for treating complex diseases. In traditional Chinese medicine (TCM), the "monarch, minister, assistant, envoy" compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas. However, due to the complex compositions and diverse mechanisms of action of TCM, it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods. Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM. Compared to resource-intensive traditional experimental methods, artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data, providing an efficient means for modeling and optimizing TCM combinations. This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships, thereby contributing to the modernization of TCM theory and methodological innovation.
Artificial Intelligence ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Drug Synergism

Objective·To investigate the potential causal relationship between type l diabetes and colorectal cancer by using Mendelian randomization(MR).Methods·Two-sample bidirectional MR was used to investigate the causal relationship between type 1 diabetes and colorectal cancer.All research data were collected from the IEU Open GWAS Project database.The dataset of type l diabetes included 9 266 cases and 15 574 controls,with correlation analysis in 12 783 129 single nucleotide polymorphisms(SNPs);the dataset of colorectal cancer included 5 657 cases and 372 016 controls,with correlation analysis in 29 999 696 SNPs.The instrumental variables SNPs were screened.The results derived from the inverse-variance weighted(IVW)method were used as the main indicator of effect.The results derived from other four methods,namely MR-Egger regression,weighted median,simple mode,and weighted mode,were used as reference.Sensitivity was analyzed with the leave-one-out method.Heterogeneity was analyzed with Cochran's Q test by using both IVW and MR-Egger methods.Pleiotropy was analyzed with MR-pleiotropy function,and Steiger test was used for directional research.The colocation analysis was used to find out whether the causal relationship between type 1 diabetes and colorectal cancer was caused by the same SNP.The genetic correlation between 2 diseases was analyzed by using the linkage disequilibrium score regression(LDSC).All tests were analyzed by using R language software(version 4.3.1).Results·After being screened,a total of 33 instrumental variables(SNPs)were used.The heterogeneity test results showed that there was heterogeneity among the SNPs(IVW and MR-Egger:P＜0.05),so the effect evaluation was based on the results of the random effect model.MR analysis showed that type 1 diabetes had a significant causal effect on colorectal cancer(P＜0.05)by using IVW,MR-Egger,weighted median and weighted mode.Sensitivity analysis showed that the results were stable.Pleiotropy was not detected in pleiotropy test(P＞0.05).Steiger test showed that the effect of type l diabetes on colorectal cancer was not interfered with by the reverse effect.Reverse MR analysis showed no causal effect of colorectal cancer on type l diabetes(P＞0.05).The results of colocalization analysis showed that the probability of H4 hypothesis was 45.7％,and the causal relationship between the 2 diseases was not caused by the same SNP in the gene sequences.LDSC analysis demonstrated that there was no genetic correlation between the two diseases.Conclusion·Type 1 diabetes may promote colorectal cancer,but colorectal cancer has no effect on type 1 diabetes.

Background::Understanding willingness to undergo pulmonary function tests (PFTs) and the factors associated with poor uptake of PFTs is crucial for improving early detection and treatment of chronic obstructive pulmonary disease (COPD). This study aimed to understand willingness to undergo PFTs among high-risk populations and identify any barriers that may contribute to low uptake of PFTs.Methods::We collected data from participants in the "Happy Breathing Program" in China. Participants who did not follow physicians’ recommendations to undergo PFTs were invited to complete a survey regarding their willingness to undergo PFTs and their reasons for not undergoing PFTs. We estimated the proportion of participants who were willing to undergo PFTs and examined the various reasons for participants to not undergo PFTs. We conducted univariable and multivariable logistic regressions to analyze the impact of individual-level factors on willingness to undergo PFTs.Results::A total of 8475 participants who had completed the survey on willingness to undergo PFTs were included in this study. Out of these participants, 7660 (90.4%) were willing to undergo PFTs. Among those who were willing to undergo PFTs but actually did not, the main reasons for not doing so were geographical inaccessibility ( n = 3304, 43.1%) and a lack of trust in primary healthcare institutions ( n = 2809, 36.7%). Among the 815 participants who were unwilling to undergo PFTs, over half ( n = 447, 54.8%) believed that they did not have health problems and would only consider PFTs when they felt unwell. In the multivariable regression, individuals who were ≤54 years old, residing in rural townships, with a secondary educational level, with medical reimbursement, still working, with occupational exposure to dust, and aware of the abbreviation "COPD" were more willing to undergo PFTs. Conclusions::Willingness to undergo PFTs was high among high-risk populations. Policymakers may consider implementing strategies such as providing financial incentives, promoting education, and establishing community-based programs to enhance the utilization of PFTs.

Asthma is a common chronic respiratory disease characterized by repeated attacks and prolonged illness.Traditional Chinese medicine believes that the formation of Sugen is the core pathogenesis of repeated asthma attacks.By tracing the origin of Sug-en theory,summarizing the connotation of ancient asthma Sugen theory and the innovative understanding of modern medical scholars on asthma Sugen,this paper explores the potential connection between the traditional Chinese medicine Sugen theory and the pathogenesis of modern asthma,in order to provide new ideas and methods for the treatment and research of asthma.

Objective To analyze the risk of return to sport in athletes using their gait data following anterior cruciate ligament re-construction(ACLR). Methods From May to June,2023,39 athletes after ACLR were recruited in Wuhan Sports University.Their data on sta-ble gait and tandem gait were recorded using a three-dimensional motion capture system,surface electromyogra-phy and a three-dimensional ergometer table.Additionally,return-to-sport scores were calculated using the K-STARTS test.The relationship between each gait indicator and the total score of the K-STARTS test was ana-lyzed with Pearson correlation analysis.Furthermore,the key indicators related to the risk of return to sport were analyzed using linear regression. Results In the stable gait test,the step time was negatively correlated with the total score of K-STARTS(r=-0.479,P=0.002),and the peak amplitude symmetry index of rectus femoris(r=0.448,P=0.004)and vastus lateralis(r=0.595,P=0.001)were positively correlated with the total score of K-STARTS.In the tandem gait test,the lateral displacement distance of the center of gravity was negatively correlated with the total score of K-STARTS(r=-0.341,P=0.034),and the time symmetry index of peak amplitude of vastus lateralis was positively correlated with the total score of K-STARTS(r=0.320,P=0.047).Regression analysis showed that the interpretation of the model based on stable gait(F=15.818,P=0.001,R2=0.650)was better than that based on tandem gait(F=7.692,P=0.001,R2=0.397). Conclusion In stable gait,gait rhythm variability and symmetry are correlated with return to sport risk.In tandem gait,gait balance and symmetry indexes are correlated with return-to-sport risk.Compared with tandem gait,the inter-pretation of return-to-sport risk assessment model based on stable gait information is better,and may be more suitable as a simple return-to-sport risk test method.

Objective To investigate the impact of human leukocyte antigen(HLA)functional epitope mismatch(EM)on the efficacy of platelet transfusion in patients with hematological diseases.Methods HLA genotyping was performed on platelet donors and patients with hematological diseases who applied for platelet serological cross-matching and HLA antigen matching from June 2021 to June 2023 by PCR-SBT method.HLA platelet matching was based on the principle of CREG to se-lect donors for patients.HLA Matchmaker 4.0 software was used to analyze donor-recipient HLA EM information.The expres-sion level and gene distribution of related HLA functional epitope(Eplet)were searched from the international HLA Epitope registry website(www.Epregistry.com.br).Retrospective analysis was conducted on clinical platelet transfusion data to evalu-ate the impact of HLA EM on platelet transfusion effectiveness.Results Platelet transfusion efficacy showed no correlation with gender and age,but it was associated with platelet matching strategy.When the total number of HLA EMs was less than 20,a lower total number of donor-recipient HLA EMs resulted in higher platelet transfusion efficiency(χ2=19.311,P=0.001)and higher average value of 24 h corrected count increment(CCI)(F=7.737,P<0.001).The total number of donor-recipient HLA EMs had negative correlation with actual 24 h CCI(Rho=-0.322,P<0.001).Further statistical analysis re-vealed that 17 Eplets were related to the effectiveness of platelet transfusion.The locus distribution of 17 Eplets might be u-nique to HLA-A(17.6%)or-B(64.7%)or shared between HLA-A and-B(17.6%),and its expression may be high(58.8%)or intermediate(41.2%).Conclusion The total number of donor-recipient HLA EMs is a crucial factor influencing platelet transfusion effectiveness,and several HLA Eplets associated with this effectiveness have been identified.

Objective:To construct an artificial intelligence (AI)-assisted system based on deep learning for corneal in vivo confocal microscopy (IVCM) image recognition and to evaluate its value in clinical applications. Methods:A diagnostic study was conducted.A total of 18 860 corneal images were collected from 331 subjects who underwent IVCM examination at Renmin Hospital of Wuhan University and Zhongnan Hospital of Wuhan University from May 2021 to September 2022.The collected images were used for model training and testing after being reviewed and classified by corneal experts.The model design included a low-quality image filtering model, a corneal image diagnosis model, and a 4-layer identification model for corneal epithelium, Bowman membrane, stroma, and endothelium, to initially determine normal and abnormal corneal images and corresponding corneal layers.A human-machine competition was conducted with another 360 database-independent IVCM images to compare the accuracy and time spent on image recognition by three senior ophthalmologists and the AI system.In addition, 8 trainees without IVCM training and with less than three years of clinical experience were selected to recognize the same 360 images without and with model assistance to analyze the effectiveness of model assistance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY2021-K148).Results:The accuracy of this diagnostic model in screening high-quality images was 0.954.Its overall accuracy in identifying normal/abnormal corneal images was 0.916 and 0.896 in the internal and external test sets, respectively.Its accuracy reached 0.983, 0.925 in the internal test sets and 0.988, 0.929 in the external test sets in identifying corneal layers of normal and abnormal images, respectively.In the human-machine competition, the overall recognition accuracy of the model was 0.878, which was similar to the average accuracy of the three senior physicians and was approximately 300 times faster than the experts in recognition speed.Trainees assisted by the system achieved an accuracy of 0.816±0.043 in identifying corneal layers of normal and abnormal images, which was significantly higher than 0.669±0.061 without model assistance ( t=6.304, P<0.001). Conclusions:A deep learning-based assistant system for corneal IVCM image recognition is successfully constructed.This system can discriminate normal/abnormal corneal images and diagnose the corresponding corneal layer of the images, which can improve the efficiency of clinical diagnosis and assist doctors in training and learning.