To conduct a scoping review of the application of generative artificial intelligence in the dietary management of patients with chronic diseases.

Systematic search was conducted in Chinese and English databases such as PubMed,Web of Science(WOS),EMbase,Cochrane Library,CINAHL,Scopus,IEEE Xplore,Association for Computing Machinery Digital Library(ACM),Chinese Biomedical Literature Service System(SinoMed),China National Knowledge Infrastructure(CNKI),WanFang Data Knowledge Service Platform,and VIP Database.The search period was from January 1,2014 to August 20,2025.Two researchers independently screened the literature according to the inclusion and exclusion criteria and extracted the basic characteristics of the literature.

A total of 22 articles were included.The studies involved a wide range of populations,with a majority being patients with diabetes.The application scope of generative artificial intelligence in patient diet management included food image recognition,diet assessment,dietary recommendations,diet intervention plan formulation,and diet consultation.The application technologies included recommendation algorithms,data mining,time series modeling,neural networks,knowledge graphs,patient profiling,deep learning,chatbots.

The application of generative artificial intelligence in the dietary management of patients with chronic diseases had achieved good results and significant advantages.However,its application was still in its infancy.In the future,large-scale clinical randomized controlled trials need to be conducted,and communication and cooperation among multiple disciplines should be strengthened.

To investigate the prevalence of intrinsic capacity decline among older adults in China and to explore the associated factors,with a specific focus on the impact of diet on their intrinsic capacity.

Based on national data from the Chinese Longitudinal Healthy Longevity Survey(CLHLS),Logistic regression analysis was employed to examine the effects of food variety and dietary diversity on the intrinsic capacity of older adults.Confounding factors were controlled for in the model construction.

A total of 3 338 older adults aged 65 years and above were included in the study.The detection rate of intrinsic capacity decline among Chinese the elderly was 67.2%.Demographic characteristics(age,living arrangement,marital status,family income,educational level,medical insurance type),lifestyle factors(smoking and drinking),and health-related indicators(body mass index,number of comorbidities,and number of oral medications taken) were significantly associated with the intrinsic capacity of older adults(all P<0.05).After adjusting for confounding factors,Logistic regression analysis revealed that fish,garlic,nuts and dietary diversity were significantly correlated with the risk of intrinsic capacity decline in the elderly(all P<0.05).

Increased frequency of fish,garlic,nuts consumption and high dietary diversity could effectively reduce the risk of intrinsic capacity decline in the elderly.

To construct a risk prediction model for gastric cancer in Sichuan province and to identify its predictive factors.

A total of 378 patients diagnosed with gastric cancer in four medical institutions in Sichuan province from July 2021 to August 2022 were selected as the case group.A total of 378 healthy individuals undergoing physical examinations during the same period were selected as the control group.The random forest method was used to construct a risk prediction model for gastric cancer.The performance of the model was evaluated by using accuracy,sensitivity,specificity,area under the ROC curve(AUC),calibration curve,and decision curve analysis(DCA).

Educational level,occupation,white meat intake,processed meat intake,coarse grain intake,pickled and sun dried food intake,dietary taste,dietary coldness and heat,eating speed,breakfast status,smoking status,alcohol consumption,tea drinking status,history of superficial gastritis,atrophic gastritis,gastric ulcer,hypertension,hyperlipidemia,family history of cancer,and personality were independent influencing factors for gastric cancer.The top three influencing factors in terms of importance score were occupation,education level,and smoking status.The accuracy of the prediction model was 99.8%.The sensitivity was 100.0%.The specificity was 99.6%.The AUC was 0.999(95%CI 0.998-1.000).The calibration curve of the prediction model indicated good consistency between the model and the actual observation results.The DCA curve suggested that the model has good clinical efficacy in predicting gastric cancer.

The risk prediction model for gastric cancer of Sichuan province constructed based on the random forest method in this study has good predictive performance and is helpful for early identification of high-risk population for gastric cancer.

Based on social ecosystems theory,to investigate the current status of nurses' social well-being in tertiary hospitals,and to analyze its potential categories and influencing factors.

A total of 658 nurses from four tertiary hospitals in Xi 'an city were selected by convenience sampling method.They were investigated by using a general information questionnaire,the Social well-being scale,the Perceptive social support scale and the Gratitude scale.Social well-being categories of them were identified through latent profile analysis.The influencing factors of each category were identified by utilizing univariate analysis and logistic regression analysis.

The Social well-being level of nurses was divided into three categories:low well-being-imbalanced(30.1%),medium well-being-chaotic(48.5%),and high well-being-positive(21.4%).Logistic regression analysis showed that working years,gratitude level,and perceiving social support are microsystem level influences of nurses' social well-being,and marital status,and affiliation to the department are microsystem level influences of nurses' social well-being.The P-values for all of these influences were less than 0.05.

The social well-being level of nurses exhibited heterogeneity.Nursing managers should build differentiated intervention programs oriented to different categories of groups based on the Social Ecosystems Theory.They should enhance the level of nurses' well-being through targeting,to form a positive incentive cycle,to drive the optimization of the quality of nursing services in hospitals,so as to improve the patients' experience of medical treatment,and to enhance healthcare service satisfaction.