Objective:To investigate whether deltoid ligament (DL) injury produces axial-plane rotational instability of the ankle in patients with chronic ankle instability (CAI).Methods:A retrospective study was conducted to analyze the 33 patients with CAI who had been treated at Department of Orthopaedics, The First Affiliated Hospital of Nanjing Medical University between January 2023 and December 2024. The cohort consisted of 17 males and 16 females with an age of (31.5±9.9) years. The patients were assigned into 2 groups based on the presence of DL injury: a lateral chronic ankle instability (LCAI) group ( n=17) and a rotational ankle instability (RAI) group ( n=16). Barefoot natural walking trials were performed in all patients. Three-dimensional kinematic data were synchronously collected using an optical motion capture system (12 cameras) and force plates. A lower extremity model was constructed to obtain shank axial rotation (internal/external rotation) and rear-foot inversion/eversion angles. Continuous relative phase (CRP) analysis was employed to assess shank-rearfoot movement coupling. The mean absolute relative phase (MARP) and deviation phase (DP) were calculated. Results:There was no statistically significant difference in the clinical baseline data between the 2 groups, indicating comparability ( P>0.05). Throughout the gait cycle, no significant differences were found in shank rotation angles or rear-foot eversion angles between the RAI group and the LCAI group. However, CRP analysis revealed that during the early stance phase (initial contact and loading response), shank-rearfoot coupling was significantly lower in the RAI group than in the LCAI group. In the early stance phase, the CRP values in the RAI group were significantly higher than those in the LCAI group. The CRP curve changes in the RAI group were consistently higher in the standce phase of the entire gait cycle than those in the LCAI group, and the peak value of the CRP curve was larger in the RAI group. Concurrently, the RAI group exhibited significantly higher MARP and DP values than the LCAI group (27.48°±14.54° versus 15.21°±9.56°; 26.02°±11.73° versus 17.83°±9.82°) (both P<0.05). Conclusion:DL injury significantly damages the axial rotational stability of the ankle joint and significantly exacerbates the mechanical instability of the ankle joint in CAI patients.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective:To investigate whether deltoid ligament (DL) injury produces axial-plane rotational instability of the ankle in patients with chronic ankle instability (CAI).Methods:A retrospective study was conducted to analyze the 33 patients with CAI who had been treated at Department of Orthopaedics, The First Affiliated Hospital of Nanjing Medical University between January 2023 and December 2024. The cohort consisted of 17 males and 16 females with an age of (31.5±9.9) years. The patients were assigned into 2 groups based on the presence of DL injury: a lateral chronic ankle instability (LCAI) group ( n=17) and a rotational ankle instability (RAI) group ( n=16). Barefoot natural walking trials were performed in all patients. Three-dimensional kinematic data were synchronously collected using an optical motion capture system (12 cameras) and force plates. A lower extremity model was constructed to obtain shank axial rotation (internal/external rotation) and rear-foot inversion/eversion angles. Continuous relative phase (CRP) analysis was employed to assess shank-rearfoot movement coupling. The mean absolute relative phase (MARP) and deviation phase (DP) were calculated. Results:There was no statistically significant difference in the clinical baseline data between the 2 groups, indicating comparability ( P>0.05). Throughout the gait cycle, no significant differences were found in shank rotation angles or rear-foot eversion angles between the RAI group and the LCAI group. However, CRP analysis revealed that during the early stance phase (initial contact and loading response), shank-rearfoot coupling was significantly lower in the RAI group than in the LCAI group. In the early stance phase, the CRP values in the RAI group were significantly higher than those in the LCAI group. The CRP curve changes in the RAI group were consistently higher in the standce phase of the entire gait cycle than those in the LCAI group, and the peak value of the CRP curve was larger in the RAI group. Concurrently, the RAI group exhibited significantly higher MARP and DP values than the LCAI group (27.48°±14.54° versus 15.21°±9.56°; 26.02°±11.73° versus 17.83°±9.82°) (both P<0.05). Conclusion:DL injury significantly damages the axial rotational stability of the ankle joint and significantly exacerbates the mechanical instability of the ankle joint in CAI patients.

Objective:To investigate the risk factors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients in plain-sand areas and loess hilly areas of Gansu province.Methods:A total of 1 599 T2DM patients who participated in chronic disease and risk factors monitoring and basic public health service management were selected by multi-stage stratified random sampling method in the sandy plain areas and loess hilly areas of Gansu province. Questionnaire survey, physical measurement and laboratory tests were performed. Multivariate binary logistic model was used to analyze the influencing factors.Results:The prevalence of DKD was 22.1% (174/787) among T2DM patients in the sandy plain areas and 19.1%(155/812) in the loess hilly area, respectively. Hypertension ( OR=3.022), hyperuricemia ( OR=2.114) and HbA1c≥7%( OR=2.231) were the risk factors for DKD in the plain-sand areas, and the risk of DKD increased with age. In the loess hilly areas, female sex ( OR=0.379) was the protective factor for DKD; while duration of disease≥10 years ( OR=2.476), hyperuricemia ( OR=1.907), HbA1c≥7% ( OR=1.927) were the risk factors for DKD; and the risk of DKD increased with the increase of age, and decreased with the increase of per capita monthly income. Conclusions:The prevalence of DKD and its influencing factors are different between sandy plain areas and loess hilly areas in Gansu province. The prevention and treatment of hypertension should be given more attention in sandy plain areas. In addition, the screening of DKD should be conducted among T2DM patients, particularly for those with old age, hyperuricemia and HbA1c≥7% in both areas of the province.

Incontinence-associated dermatitis is one of the common complications in critically ill patients.This paper reviews the research progress of risk prediction models for incontinence-associated dermatitis in critically ill patients,introduces and compares the characteristics and application effects of different risk prediction models.The purpose is to provide ideas for constructing a localized risk prediction model and provide evidence for medical staff to identify risk factors of incontinence-associated dermatitis at an early stage and take preventive measures.

AIM: The main chemical components of Yufang Fangji II (Hubei Fang) of COVID-19 were studied systematically and combined with network pharmacology to provide a reference for the study of its effective substances. METHODS: Ultra high-performance liquid chromatography quadrupole time of flight mass spectrometry (UHPLC-Q-TOF/MS) was applied to identify the absorbed components of the prescription in rat plasma. TCMSP database and Swiss Target Prediction data platform were used to predict the target of the identified blood components, and network visualization software Cytoscape 3.7.2 was used draw the association network diagram, and GO enrichment analysis and KEGG pathway enrichment analysis were conducted for the key targets. With the help of CB-Dock online molecular docking platform, the molecular docking of key targets and blood entering compounds was carried out, and the docking combination with good affinity value was displayed by ligplot software to verify the preventive effect of Yufang Fangji II on COVID-19. RESULTS: A total of 52 chemical components identified in the prescription, in which 13 components were absorbed in the rat plasma as the prototype, and they were from Astragalus membranaceus, Atractylodes macrocephala, Saposhnikoviae Radix, Lonicerae Japonicae Flos, and Citri Reticulatae Pericarpium, respectively. These compounds were recognized to act on 17 core targets, including mapk3, TNF and other targets related to inflammation, MPO and other targets related to oxidative stress, VEGFR, KDR and other targets related to vascular endothelium. The results of molecular docking showed that the absorbed components had good binding activity with the key targets. CONCLUSION: Compounds in Yufang Fangji II are involved in regulating inflammation, oxidative stress, vascular and cellular physiological activities, which have preventive effects on COVID-19 through regulating IL-17, PI3K Akt, MAPK and other pathways.

In the battle of COVID-19 epidemic prevention and control in China, the "countermarching people" faced up to all difficulties and went forward one after another to the forefront of anti-epidemic. Many vivid figures and examples have aroused our feelings and thoughts on life. Under the background of this special period, medical colleges should take the COVID-19 prevention and control as an opportunity to enrich the contents of bioethics education, blend in patriotism education, social responsibility education, medical moral education, and life and death culture education. By strengthening the construction of teaching staff, attaching importance to the integration of teaching contents, optimizing teaching carriers, expanding practical activities and recommending online teaching resources, the teaching reform of bioethics education is explored in order to enhance the effectiveness and sustainability of bioethics education and moral education.

Objective This study explored the association between air pollution exposure and birth weight by using the multilevel linear model,after controlling related meteorological factors and individual differences of both mothers and babies.Methods Women of childbearing age who were pregnant in Xi'an from 2010 to 2013,were selected as objects of this study.Multistage random sampling method was used to select 4 631 subjects followed by a self-designed questionnaire survey.Data related to quality of air and meteorology were gathered from routine monitoring system.Gestational age and date of birth,together with the average levels of air pollution were calculated for each trimester on each mother,and then the impact of air pollution on birth weight was assessed.A multilevel linear model was employed to investigate the association between the levels of exposure to air pollution by birth weight.Confounding factors were under control.We established three models in this study:Model 1 which involving the variable of air pollution exposure.Model 2 was adjusted for variables in Model 1 plus some other individual differences of both mother and baby.Model 3 was adjusted for variables in Model 2 plus meteorological factors.Results There were significant differences seen in birth weight within the subgroups of gender,gestational age,mother's reproductive age,maternal education,residential areas and family incomes (P＜0.01) of the infants.However,there was no difference found in Model 1 (P＞0.05).Data from Model 3 indicated that a decrease of 13.3 g (10.9 g in Model 2) and 6.6 g (5.9 g in Model 2) in birth weight that were associated with an increase of 10 μg/m3 in the average level of NO2 and PM10 during the second trimester;A decrease of 13.7 g (9.8 g in Model 2) in birth weight was associated with an increase of 10 μg/m3 in the average level of NO2 during the third trimester.Conclusion After controlling for meteorological factors,the levels of exposure to NO2 and PM10 during the second trimester and NO2 during the third trimester were negatively associated with birth weight.