Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.

This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a pilot city of China from 2017 and followed up to 2021, the study included 42,353 disabled adults aged over 65, with 25,071 assigned to the training set and 17,282 to the validation set. The administrative data (including baseline characteristics, underlying medical conditions, and all-cause mortality) were collected to develop a deep learning model by least absolute shrinkage and selection operator. After a median follow-up time of 14 months, 17,565 (41.5%) deaths were recorded. Thirty predictors were identified and included in the final models for disability-related deaths. Physical disability (mobility, incontinence, feeding), adverse events (pressure ulcers and falls from bed), and cancer were related to poor prognosis. A total of 10,127, 25,140 and 7086 individuals were classified into low-, medium-, and high-risk groups, with actual risk probabilities of death of 9.5%, 45.8%, and 85.5%, respectively. This deep learning model could facilitate the prevention of risk factors and provide guidance for long-term care model planning based on risk stratification.

The effect of group IV chitinase, HaCHT4, on the chitin content of the peritrophic matrix (PM) during larval growth and development of Helicoverpa armigera.

BACKGROUND: Extensive research has been conducted on insect chitinases. However, little is known about the function of chitinase in the regulation of the surface structure of the peritrophic matrix (PM) in larval midguts. The aim of this study was to analyze the effect of HaCHT4 on the chitin content and surface structure of the PM during larval growth and development of Helicoverpa armigera. RESULTS: The expression level of HaCHT4 was lower and the chitin content was higher in the early stages of fourth to sixth instar larvae, but they were reversed in the corresponding late stages. The correlation coefficient between the expression level of HaCHT4 and the chitin content was -0.585 (P < 0.05), with a higher negative correlation of -0.934 for the fourth instar (P < 0.01). Scanning electron microscopy (SEM) showed that the surface structure of PM was multi-laminated with small pores in the early stages of fourth to sixth instar larvae, and more and bigger pores in the late stages. Low expression of HaCHT4 caused by RNA interference (RNAi) resulted in the increase of chitin content in the PM, and the surface structure of PM became multilayered with smaller pore size in the late stage of fourth instar larvae. Also, induction of HaCHT4 by application of 2-tridecanone (2-TD), decreased the chitin content of PM, caused larger pores to form and lots of food bolus to attach to the PM surface, and also increased the larval susceptibility to chlorantraniliprole. CONCLUSION: These results provided strong evidence that HaCHT4 plays an important role by regulating the chitin content of the PM and its surface structure, thereby affecting the sensitivity of H. armigera to chlorantraniliprole.