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.

Research and application of single-cell sequencing in tumor heterogeneity and drug resistance of circulating tumor cells.

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.

Association of Genetic Polymorphisms in FOXA1 with the Progression of Genetic Susceptibility to Gastric Cancer.

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.

N-methyladenosine (mA) RNA modification in gastrointestinal tract cancers: roles, mechanisms, and applications.

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, mA methylation has attracted increasing research interest in recent years. mA is catalysed by RNA methyltransferases METTL3, METTL14 and WTAP (writers), mA is removed by the demethylases FTO and ALKBH5 (erasers) and interacts with m6A-binding proteins, such as YT521-B homology (YTH) domain-containing proteins.