BACKGROUND:Inter-limb asymmetry is a common phenomenon observed during human growth and development.Prolonged specialized training can lead to specific adaptations in inter-limb asymmetry among athletes.OBJECTIVE:To review the formation causes,manifestations,and impacts of inter-limb asymmetry on sports performance,and provide an overview of the relevant assessment methods and intervention strategies.METHODS:A literature search was conducted in the CNKI,WanFang,PubMed,and Web of Science databases from their inception to September 2024.The search terms included"asymmetry,asymmetries,asymmetric,asymmetrical,imbalance,strength,power,force,jump,sprint,athletic performance,anthropometry,injury"in English and Chinese.After excluding duplicate publications,irrelevant content,and conference papers,a total of 131 articles were finally included for analysis.RESULTS AND CONCLUSION:(1)Inter-limb asymmetry can be influenced by various factors including genetics,task demands,training regimens,injuries,fatigue,and limb preference.These factors lead to being primarily manifested in anatomical structure,strength performance,and task-specific asymmetry.(2)An increase in inter-limb asymmetry can result in impaired performance in bilateral in-phase symmetric movements.However,the relationship between increased inter-limb asymmetry and bilateral out-of-phase symmetric movements remains unclear and requires further investigation.(3)Training interventions have been shown to effectively mitigate inter-limb asymmetry,with unilateral training demonstrating superior outcomes compared with bilateral training.The choice of training methods and content should be tailored to meet the specific demands of the sport.(4)To further clarify the relationship between inter-limb asymmetry and athletic performance,it is recommended that future research adopt the concept of"task specificity"in inter-limb asymmetry.This includes standardizing study designs,selecting sensitive testing methods and indicators,unifying calculation methods to provide more high-quality evidence,and establishing categorized warning threshold standards for inter-limb asymmetry in different sports.

Objective To construct a prediction model for the population with hypertension and diabetes to assess the risk of chronic kidney disease (CKD), and to provide a scientific basis for formulating targeted CKD prevention and control measures. Methods Based on physical examination data from community residents aged 60 years and above in Nanjing in 2022, 10 221 patients with hypertension and diabetes were selected as the study subjects. Variables associated with CKD prevalence were screened using univariate analysis, and further variable selection was performed using LASSO regression. Finally, a CKD risk prediction model was constructed based on logistic regression. The model's performance was evaluated using the ROC curve and calibration curve. Results The prevalence rate of CKD in the study population was 22.71%, with a mean age of 71.66 years. LASSO regression identified seven variables associated with CKD: age, blood urea nitrogen (BUN), hemoglobin, uric acid, triglyceride-glucose (TyG) index, urine protein-to-creatinine ratio (UPCR), and medical insurance type. The final logistic regression model incorporated six variables: age [OR=1.067 (95% CI: 1.058-1.076)], BUN [OR=1.377 (95% CI: 1.338-1.418)], hemoglobin [OR=0.992 (95% CI: 0.989-0.995)], uric acid [OR=1.004 (95% CI: 1.003-1.004)], TyG index [OR=1.445 (95% CI: 1.324-1.577)], and self-payment medical insurance [OR=1.732 (95% CI: 1.542-1.945)]. The model had an AUC of 0.759 (95% CI: 0.747-0.770) and a Brier score of 0.140 (95% CI: 0.136-0.145), indicating good predictive performance. The calibration curve showed good agreement between the predicted risk and the observed value. Conclusion The constructed LASSO-logistic regression risk prediction model in this study can effectively assess the risk of CKD in elderly individuals aged 60 years and above with hypertension and diabetes, providing a basis for early identification of high-risk individuals and the formulation of targeted CKD prevention and control measures.

BACKGROUND:Inter-limb asymmetry is a common phenomenon observed during human growth and development.Prolonged specialized training can lead to specific adaptations in inter-limb asymmetry among athletes.OBJECTIVE:To review the formation causes,manifestations,and impacts of inter-limb asymmetry on sports performance,and provide an overview of the relevant assessment methods and intervention strategies.METHODS:A literature search was conducted in the CNKI,WanFang,PubMed,and Web of Science databases from their inception to September 2024.The search terms included"asymmetry,asymmetries,asymmetric,asymmetrical,imbalance,strength,power,force,jump,sprint,athletic performance,anthropometry,injury"in English and Chinese.After excluding duplicate publications,irrelevant content,and conference papers,a total of 131 articles were finally included for analysis.RESULTS AND CONCLUSION:(1)Inter-limb asymmetry can be influenced by various factors including genetics,task demands,training regimens,injuries,fatigue,and limb preference.These factors lead to being primarily manifested in anatomical structure,strength performance,and task-specific asymmetry.(2)An increase in inter-limb asymmetry can result in impaired performance in bilateral in-phase symmetric movements.However,the relationship between increased inter-limb asymmetry and bilateral out-of-phase symmetric movements remains unclear and requires further investigation.(3)Training interventions have been shown to effectively mitigate inter-limb asymmetry,with unilateral training demonstrating superior outcomes compared with bilateral training.The choice of training methods and content should be tailored to meet the specific demands of the sport.(4)To further clarify the relationship between inter-limb asymmetry and athletic performance,it is recommended that future research adopt the concept of"task specificity"in inter-limb asymmetry.This includes standardizing study designs,selecting sensitive testing methods and indicators,unifying calculation methods to provide more high-quality evidence,and establishing categorized warning threshold standards for inter-limb asymmetry in different sports.

The application of immunosuppressants has significantly reduced the incidence rate of rejection reaction after liver transplantation， but the clinical efficacy of immunosuppressants is greatly affected by individual differences between patients. This article systematically reviews the recent research advances in the interaction between immunosuppressants and gut microbiota， with a focus on the regulatory role and mechanism of intestinal microflora communities on the efficacy of immunosuppressants. Studies have shown that intestinal microbiome is one of the key factors influencing the efficacy of immunosuppressive therapy after liver transplantation. This review aims to provide a theoretical basis for in-depth research in this field and provide new insights for developing individualized immunosuppressive treatment regimens based on the regulation of intestinal microflora.

ObjectiveTo construct and validate a risk assessment model for post-transplant diabetes mellitus （PTDM） using multiple machine learning algorithms， and to realize the early identification of PTDM. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent allogeneic liver transplantation in The First Hospital of Shanxi Medical University from April 1， 2020 to December 31， 2024， and they were randomly divided into a training set and a validation set at a ratio of 7∶3. The LASSO regression analysis combined with 5-fold cross-validation was used for feature selection. Seven machine learning models were developed in the training set， i.e.， logistic regression （LR）， decision tree （DT）， Naive Bayes （NB）， random forest （RF）， K-nearest neighbor （KNN）， extreme gradient boosting （XGBoost）， and adaptive boosting （AdaBoost）. In the validation set， various methods were used to assess the predictive performance of each model， such as accuracy， precision， recall rate， specificity， F1 score， area under the receiver operating characteristic curve （AUROC）， and area under the precision-recall curve （AUPRC）. The Brier score and decision curve analysis were used to assess the calibration and clinical practicability of the models， and the SHAP method was used to analyze feature importance. The independent-samples t test or the Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsA total of 135 liver transplant recipients were enrolled， among whom 26 developed PTDM， and there were 94 patients in the training set and 41 in the validation set. Feature extraction and screening identified 7 key features of sex， overweight or obesity， anhepatic phase， time of operation， length of hospital stay， early postoperative hypomagnesemia， and impaired fasting glucose （IFG）. In the validation set， the XGBoost model showed the best predictive performance， with an AUROC of 0.907 （95% confidence interval ［CI］： 0.807 — 0.989）， an AUPRC of 0.649 （95%CI： 0.339 — 0.955）， an accuracy of 0.878， a precision of 0.667， a recall rate of 0.750， an F1-score of 0.706， a specificity of 0.909， and a Brier score of 0.104. The decision curve analysis showed that when the threshold probability was below 0.667， application of the XGBoost model in clinical decision-making provided relatively high net benefit. The SHAP analysis showed that the length of hospital stay ranked first in terms of feature importance， followed by time of operation， overweight or obesity， sex， IFG， anhepatic phase， and early postoperative hypomagnesemia. ConclusionThe risk assessment model for PTDM in liver transplant recipients based on XGBoost algorithm has excellent performance and can effectively identify high-risk individuals； however， multicenter large-sample data are needed for further validation.

Based on the actual situation of rapid increase in obesity prevalence and the current lack of a professional weight loss and bariatric surgery treatment platform in Macao, coupled with the continouous rise in the obesity population, the further development and refine-ment of obesity treatment methods has become particularly urgent. Against this backdrop, the authors conduct an in-depth discussion to analyze how Macao, leveraging its unqiue geographical location and favorable policy advantages within the broader context of collaborative development in the Guangdong-Hong Kong-Macao greater bay area, can actively explore future development paths and potential challenges in the fields of bariatric and metabolic medicine and surgery, with the aim to provide a robust reference for advancing related medical technologies in Macao, thereby enhancing the overall level of obesity treatment in the region.

Objective To examine the level of sperm flagellar tubulin glycosylation(Gly-Tubulin)in asthenospermia patients(AP)and discuss its significance.Methods According to the principle of inclusion and exclusion criteria,108 patients treated in the Department of Andrology in Xinjiang Uygur Autonomous Region Maternal and Child Health Hospital from June 2021 to May 2022 were rolled in this study.According to the results of semen examine,they were divided into group AP(n=74),and normal control(NC)group(n=34).Colleced the clinical date of both group,such as the age,the sterility period.The levels of sex hormones were detected by chemiluminescence method.The glycosylated Tubulin levels of the sperm were detected by Western blot and immunofluorescence was used for localization analysis,and the relationship between the Gly-Tubulin of sperm flagellar and asthenospermia was analyzed by Multivariate Logistic regression analysis.Results Compared to NC group,the sterility period of group AP was prolong(3.98±0.30 vs 0.78±0.08),the percent of forward sperm motility was decreased(18.55±0.97 vs 46.24±1.89)and the expression level of Gly-Tubulin in the sperm flagellar was decreased(0.64±0.11 vs 1.00±0.00),the differences with statistical significance(t=62.038,χ2=14.394,3.148,all P＜0.05).There were no significant differences in follicle-stimulating hormone(FSH),luteinizing hormone(LH),prolactin(PRL),estradiol(E2),testosterone,semen volume,sperm concentration and total sperm between the two groups(t/χ2=0.043～1.503,all P>0.05).Multivariate Logistic regression analysis showed that the percent of forward sperm motility and the sperm flagellar Gly-Tubulin expression level were independent risk factors for male asthenospermia(Wald χ2=4.406,7.492,P<0.05).Conclusion Abnormal level of Gly Tubulin in sperm flagella may be closely related to the occurrence and development of asthenospermia.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To investigate the cerebral protective role of Duzhi pills on ischemic stroke rats and its mechanisms.Methods The healthy male SD rats were assigned to 5 groups:sham group,model group,Duzhi pill low-dose group(1.0 g/kg a day),Duzhi pill high-dose group(2.0 g/kg a day),or argatroban group(positive control).The low-dose and high-dose groups of Duzhi pills were intragastrically administered once a day for 8 d,while the other groups were intragastrically administered with the same amount of normal saline.Except the sham group,the middle cerebral artery occlusion(MCAO)cerebral ischemia-reperfusion model was established by suture method at 0.5 h after intragastric administration on the 8th day in other groups,and the ischemia duration was 90 min.Argatroban group was given a single dose(3.0 mg/kg)of argatroban via caudal vein at the same time of reperfusion.After 24 h of cerebral ischemia,the area of cerebral infarction,degree of cerebral infarction and neurological function injury were evaluated by 2,3,5-triphenyltetrazolium chloride(TTC)staining,Nissl staining and neurobehavioral score,respectively.The levels of inflammatory factors(transforming growth factor-β1[TGF-β1]and interleukin-1β[IL-1β])in the brain after cerebral ischemia were detected by enzyme-linked immunosorbent assay(ELISA),and the coagulation indexes were detected by an automatic coagulation analyzer.The scavenging ability of Duzhi pills on free radicals was analyzed by 2,2-diphenyl-1-picrylhydrazyl(DPPH)scavenging experiment.Results High-dose Duzhi pills reduced the cerebral infarction area,decreased the deep Nissl staining score of neural cells,and improved the symptoms of neurobehavioral defects in MCAO rats(compared with the model group,all P＜0.01).The levels of IL-1β and TGF-β1 in the ischemic brain tissue of the model group were significantly increased(compared with the sham group,both P＜0.01),while the levels of IL-1β and TGF-β1 in the brain tissue of the rats treated with low-dose and high-dose Duzhi pills were significantly decreased(compared with the model group,all P＜0.01).The coagulation index fibrinogen level in the model group was significantly higher than that in the sham group(P＜0.01),and both low-dose and high-dose Duzhi pills could inhibit the production of fibrinogen(compared with the model group,both P＜0.01).DPPH scavenging test results showed that Duzhi pills could scavenge free radical,the half inhibitory concentration was(1.33±1.11)mg/mL,and the slope of the curve was 1.378±0.145.Conclusion Duzhi pills play a cerebral protective role in ischemic stroke rats.The mechanism may be related to the inhibition of fibrinogen production,the reduction of inflammatory factors(TGF-β1 and IL-1β),and the antioxidant effects.

Objective To screen for key genes and important pathways common for different etiologies of acute kidney injury(AKI)by pathway-associated deep neural network and multiple machine learning algorithms.Methods AKI microarray datasets GSE30718,GSE37838,GSE53769,GSE108113,GSE125779,GSE99325,and GSE174020 downloaded from the Gene Expression Omnibus(GEO)database were merged,including 60 kidney samples from AKI patients and 79 kidney samples from healthy controls.They were divided(8∶2)into training sets and test sets,and were used to train and evaluate pathway-associated deep neural network and 4 machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),random forest(RF),support vector machine-recursive feature elimination(SVM-RFE),and extreme gradient boosting(XgBoost),to screen for common key genes and pathways of different etiologies of AKI.The downloaded datasets GSE99340 and GSE1563 were merged,including 43 kidney samples from AKI patients and 36 kidney samples from healthy controls,which were used as external validation sets for LASSO model and nomogram performance test based on the final screened genes.The pathway-associated deep neural network and machine learning algorithms were evaluated using receiver operating characteristic curves,precision,recall,accuracy,and F1-score.The immune cell infiltration characteristics were explored in AKI via cell-type identification by estimating relative subsets of RNA transcripts(CIBERSORT),and Pearson correlation coefficients were used to evaluate the correlation between the final screened common key genes and immune cell infiltration levels.Results The pathway-associated deep neural network trained by 5-fold cross validation produced an area under curve(AUC)of 0.914 5±0.007 0,a precision of 0.750 0±0.044 0,a recall of 0.923 1±0.048 0,an accuracy of 0.838 7±0.016 0,and an F1-score of 0.827 6±0.020 0 in the test set,yielding a robust and highly accurate classification performance for AKI,and identified key pathways and a subset of candidate genes.The 4 machine learning algorithms all achieved high discriminative performance for AKI in the test set with AUC≥0.860,precision≥0.750,recall≥0.800,and F1-score≥0.774,and screened 7 common key genes for AKI with different etiologies,including CD86,C-X-C motif chemokine ligand 10(CXCL10),dynamin 2(DNM2),proto-oncogene FOS,transcription factor 12(TCF12),VGF nerve growth factor inducible(VGF),and A kinase anchoring protein 5(AKAP5).Based on the final screened common key genes,the LASSO model had an AUC of 0.940 4 for the test set and an AUC of 0.944 4 for the external validation,and the model showed a very high discriminatory ability for the AKI,which demonstrated the overall regulatory performance of the genes.The nomogram constructed based on the screened 7 genes demonstrated the highest classification performance with an AUC of 0.928 9,validating the outstanding contribution and overall action performance of the screened individual genes.Immune cell infiltration analysis showed that there were significant differences in B cells na?ve,mast cells activated,monocytes,macrophages M1,B cells memory,and dendritic cells activated between AKI samples and healthy control samples(all P＜0.05).Macrophages M1 and monocytes were positively correlated with CD86 and CXCL10,mast cells activated were positively correlated with FOS,and B cells na?ve were negatively correlated with CD86 and CXCL10(all P＜0.01).Mast cells activated were positively correlated with VGF and negatively correlated with CD86 and TCF12,while memory B cells were positively correlated with CD86(all P＜0.05).Conclusion Strategy combining pathway-associated deep neural network and multiple machine learning classifiers can mine high-value key genes from high-dimensional,complex and heterogeneous transcriptomic data as potential targets for therapeutic interventions in AKI.