Xanthohumol Regulates Mitophagy in Osteosarcoma Cells via AMPK-ULK1-FUNDC1 Signaling Pathway.

Osteosarcoma (OS) is the most common primary bone malignancy. The therapeutic efficacy for OS patients has remained stagnant in recent decades. Xanthohumol (XN), a flavonoid naturally found in hops, has demonstrated significant anticancer properties in lung and breast cancer.

The association between gut microbiota and accelerated aging and frailty: a Mendelian randomization study.

Background: The recent observational studies have unveiled the correlation between the composition and dynamic alterations of the gut microbiome and aging; however, the causal relationship remains uncertain.

Aims: The objective of this study is to investigate the causal relationship between the gut microbiome and accelerated aging as well as frailty, from a genetic perspective.

Methods: We obtained data on the gut microbiome, intrinsic epigenetic age acceleration, and Frailty Index from published large-scale genome-wide association studies.

Improving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement.

Lung cancer remains a major global health challenge, and accurate pathological examination is crucial for early detection. This study aims to enhance hyperspectral pathological image analysis by refining annotations at the cell level and creating a high-quality hyperspectral dataset of lung tumors. We address the challenge of coarse manual annotations in hyperspectral lung cancer datasets, which limit the effectiveness of deep learning models requiring precise labels for training.

MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.

Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning.

A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.

Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data.

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression.