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Abstract 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.

Abstract Malignant tumor is a largely harmful disease worldwide. The cure rate of malignant tumors increases with the continuous discovery of anti-tumor drugs and the optimisation of chemotherapy options. However, drug resistance of tumor cells remains a massive obstacle in the treatment of anti-tumor drugs. The heterogeneity of malignant tumors makes studying it further difficult for us. In recent years, using single-cell sequencing technology to study and analyse circulating tumor cells can avoid the interference of tumor heterogeneity and provide a new perspective for us to understand tumor drug resistance.

Abstract Analogous to DNA methylation and histone modification, RNA modification, as another epigenetic layer, plays an important role in many diseases, especially in tumours. As the most common form of RNA modification, m6A methylation has attracted increasing research interest in recent years. m6A is catalysed by RNA methyltransferases METTL3, METTL14 and WTAP (writers), m6A is removed by the demethylases FTO and ALKBH5 (erasers) and interacts with m6A-binding proteins, such as YT521-B homology (YTH) domain-containing proteins. This article reviews recent studies on methylation modification of m6A in gastrointestinal tract cancers.

Objective. To investigate the relationship between polymorphism of FOXA1 gene rs12894364 and rs7144658 and susceptibility to gastric cancer. Methods. A case-control study was conducted to select 577 cases of primary gastric cancer and 678 cases of normal control. We extracted whole blood genomic DNA and amplified the target gene fragment by PCR. The genotyping and allele was tested through a snapshot method. Results. There was no significant difference in the frequency distribution of genotype between the case group and control group (P>0.05). Stratified analyses showed the SNPs were not correlated with the susceptibility of GC according to different age, gender, cigarette smoking, and alcohol drinking status. Conclusion. There is no significant correlation between the polymorphisms of FOXA1 gene rs12894364 and rs7144658 and the risk of gastric cancer.

A novel and efficient Ugi four‐component domino dicyclization strategy has been developed for one‐step constructing diverse 3‐(1,3,4‐oxadiazol‐2‐yl)isoindolin‐1‐ones with good yields, without the need for additional additives. This domino dicyclization process underwent three consecutive reactions including Ugi, aza‐Wittig and N‐acylation by utilizing (N‐isocyanimine)triphenylphosphorane, methyl 2‐formylbenzoate, primary amines, and carboxylic acids. The initial biological activity testing revealed that compound 5 i exhibited good antifungal activity against certain fungi and demonstrated versatile applications in the fields of pesticide chemistry. [ABSTRACT FROM AUTHOR]