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.

An overview of PROTACs targeting MDM2 as a novel approach for cancer therapy.

MDM2 genes amplification or altered expression is commonly observed in various cancers bearing wild-type TP53. Directly targeting the p53-binding pocket of MDM2 to activate the p53 pathway represents a promising therapeutic approach. Despite the development of numerous potent MDM2 inhibitors that have advanced into clinical trials, their utility is frequently hampered by drug resistance and hematologic toxicity such as neutropenia and thrombocytopenia.

Intra-Host Co-Existing Strains of SARS-CoV-2 Reference Genome Uncovered by Exhaustive Computational Search.

The COVID-19 pandemic caused by SARS-CoV-2 has had a severe impact on people worldwide. The reference genome of the virus has been widely used as a template for designing mRNA vaccines to combat the disease. In this study, we present a computational method aimed at identifying co-existing intra-host strains of the virus from RNA-sequencing data of short reads that were used to assemble the original reference genome.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior.

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness.

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.

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design.

Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer.