Objective To construct a nomogram prediction model for the risk of mild cognitive impair-ment (MCI) in elderly people aged ≥ 60-year-old.Methods A total of 502 elderly permanent residents in Guangxi were selected as the research subjects by the multi-stage stratified random sampling method,and the general situation questionnaire and the Beijing edition of MoCA-BJ scale were used to investigate the elderly people,and their anthropometric indicators were collected.The minimum absolute shrinkage rate and selection operator (LASSO) regression were used to screen the characteristic variables.The MCI risk nomogram pre-diction model was constructed.The receiver operating characteristic (ROC) curve and calibration curve were adopted to conduct the fitting effect test on the prediction model.Results Among the 502 elderly people,244 cases (46.04％) had the normal cognition and 258 cases (48.68％) had MCI.The logistic regression analysis showed that the age,education background,month income,children support,calf circumference,BMI and body fat index were the influencing factors of MCI in the elderly people,and the nomogram prediction model of the MCI risk in the elderly people was constructed by these seven variables.The area under the ROC curve (AUC) of the model was 0.790 (95％CI:0.750-0.829),the sensitivity was 0.64,the specificity was 0.62,the C-index index was 0.790,and the model fitting x2=8.111,P=0.454,the predictive value was basically consistent with the actual value.Conclusion The nomogram prediction model of MCI risk in the elderly peo-ple is successfully constructed with good predictive effect.

Objective To compare the efficiency of nomogram and different machine learning algo-rithms for constructing the dental caries predictive models for middle-aged and elderly people.Methods The multi-stage stratified random sampling method was used to select 510 middle-aged and elderly people from Nanning City,Guigang City and Chongzuo City as the research subjects for conducting the questionnaire sur-vey and oral cavity examination.The univariate analysis and Lasso regression were used to screen the related variables,and the multivariate logistic regression analysis was used to determine the final independent influen-cing factors.Based on the salient features,the nomogram predictive model was established,and the seven ma-chine learning algorithms,including linear discriminant analysis (LDA),partial least squares (PLS),range Doppler algorithm (RDA),generalized linear models (GLM),random forest (RF),support vector machine (SVM) kernel function (SVM-Radial),and SVM linear kernel function (SVM-Linear),were used to construct the seven kinds of dental caries risk predictive models.The area under the receiver operating characteristic (ROC) curve (AUC) was adopted to evaluate the predictive performance of various models and the predictive performance of models constructed using different variable screening methods.Results The detection rate of dental caries in middle-aged and elderly people was 71.18％.After feature screening,the five predictive factors were ultimately retained,which were the age (OR=0.945,95％CI:0.917-0.973),brushing frequency (OR=0.688,95％CI:0.475-0.997),whether having teeth cleaning in the past one year (OR=0.303,95％CI:0.103-0.890),number of remaining teeth (OR=1.062,95％CI:1.038-1.087) and oral health assess-ment tool (OHAT) score (OR=1.363,95％CI:1.234-1.505).The results of comparison of various models showed that the predictive model constructed by the RF algorithm performed the best,the median of AUC was 0.747,followed by the nomogram,and the median of AUC was 0.733.The median of AUCs in the predic-tion model constructed by single factor+Lasso+multivariate logistic (Lasso+logistic) screening independent variables were higher than those constructed by RF algorithm screening independent variables.ConclusionBased on Lasso+logistic screening variables,RF provide more reliable predictive efficiency in predicting dental caries in middle-aged and elderly people than nomogram and the other machine learning algorithms.

Circadian rhythm refers to the 24-hour periodic changes in behavior, physiology, and molecular processes in the human body. Disruptions to the circadian rhythm not only affect mental health but are also associated with various metabolic disorders, including the regulation of bone and muscle metabolism. Research has shown that work-related musculoskeletal disorders (WMSDs) are influenced not only by workload but also by circadian rhythm factors, such as shift work. This review examined the relationships between circadian rhythm-related antecedents, outcomes, and WMSDs, exploring their shared metabolic markers and mechanisms. It provided a systematic overview of the intrinsic connection between circadian rhythm disruptions and WMSDs. While current studies highlight the impact of circadian rhythm disturbances on musculoskeletal disorders, further investigation is required to address the confounding factors involved. Future research should integrate chronobiology with both subjective and objective data to explore the pathway from environmental factors to intermediate phenotypes to diseases, ultimately providing a more comprehensive understanding of the network mechanisms underlying WMSDs.