Diet and lifestyle behaviours simultaneously act on frailty: it is time to move the threshold of frailty prevention and control forward.

BACKGROUND: To analyse the association among the simultaneous effects of dietary intake, daily life behavioural factors, and frailty outcomes in older Chinese women, we predicted the probability of maintaining physical robustness under a combination of different variables. METHODS: The Fried frailty criterion was used to determine the three groups of "frailty", "pre-frailty", and "robust", and a national epidemiological survey was performed. The three-classification decision tree model was fitted, and the comprehensive performance of the model was evaluated to predict the probability of occurrence of different outcomes. RESULTS: Among the 1,044 participants, 15.9% were frailty and 50.29% were pre-frailty; the overall prevalence first increased and then decreased with age, reaching a peak at 70-74 years of age. Through univariate analysis, filtering, and embedded screening, eight significant variables were identified: staple food, spices, exercise (frequency, intensity, and time), work frequency, self-feeling, and family emotions. In the three-classification decision tree, the values of each evaluation index of Model 3 were relatively average; the accuracy, recall, specificity, precision, and F1 score range were between 75% and 84%, and the AUC was also greater than 0.800, indicating excellent performance and the best interpretability of the results. Model 3 takes exercise time as the root node and contains 6 variables and 10 types, suggesting the impact of the comprehensive effect of these variables on robust and non-robust populations (the predicted probability range is 6.67-93.33%). CONCLUSION: The combined effect of these factors (no exercise or less than 0.5 h of exercise per day, occasional exercise, exercise at low intensity, feeling more tired at work, and eating too many staple foods (> 450 g per day) are more detrimental to maintaining robustness.

Big data on the prevalence of injury deaths among 187 488 elderly Chinese in the past 20 years (2000-2020): a systematic review and meta-analysis.

BACKGROUND: This study systematically reviewed injury death and causes in the elderly population in China from 2000 to 2020, to prevent or reduce the occurrence of injuries and death. METHODS: The CNKI, VIP, Wan Fang, MEDLINE, Embase, SinoMed, and Web of Science databases were searched to collect epidemiological characteristics of injury death among elderly over 60 years old in China from January 2000 to December 2020. Random effects meta-analysis was performed to pool injury mortality rate and identify publication bias, with study quality assessed using the AHRQ risk of bias tool. RESULTS: (1) A total of 41 studies with 187 488 subjects were included, covering 125 million elderly. The pooled injury mortality rate was 135.58/105 [95%CI: (113.36 to 162.14)/105], ranking second in the total death cause of the elderly. (2)Subgroup analysis showed that male injury death (146.00/105) was significantly higher than that of females (127.90/105), and overall injury mortality increased exponentially with age (R2 = 0.957), especially in those over 80 years old; the spatial distribution shows that the injury death rate in the central region is higher than that in the east and west and that in the countryside is higher than that in the city; the distribution of death time shows that after entering an aging society (2000-2020) is significantly higher than before (1990-2000). (3) There are more than 12 types of injury death, and the top three are falling, traffic accidents, and suicide. CONCLUSIONS: China's elderly injury death rate is at a high level in the world, with more males than females, especially after the age of 80. There are regional differences. The main types of injury death are falling, traffic, and suicide. During the 14th Five-Year Plan period, for accidental injuries and death, a rectification list for aging and barrier-free environments was issued. PROSPERO REGISTRATION: The systematic review was registered in PROSPERO under protocol number CRD42022359992.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer.

Objective: Using visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated. Methods: On April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used. Results: A total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal "New England Journal of Medicine" of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future. Conclusions: An increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.