Metallic nanoparticles (MNPs) are new engineering materials with broad prospects for biomedical applications; thus, their biosafety has drawn great concern. The liver is the main detoxification organ of vertebrates. However, many issues concerning the interactions between MNPs and biological systems (cells and tissues) are unclear, particularly the toxic effects of MNPs on hepatocytes and other liver cells. Numerous researchers have shown that some MNPs can induce decreased cell survival rate, production of reactive oxygen species (ROS), mitochondrial damage, DNA strand breaks, and even autophagy, pyroptosis, apoptosis, or other forms of cell death. Our review focuses on the recent researches on the liver toxicity of MNPs and its mechanisms at cellular and subcellular levels to provide a scientific basis for the subsequent hepatotoxicity studies of MNPs.

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Object. This study is aimed at constructing a deep learning architecture of the autoencoder to integrate multiomics data and identify the risk of patients with stomach adenocarcinoma. Methods. Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k -means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model’s robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints. Results. The patients were divided into low-risk and high-risk groups according to the k -means clustering algorithm. The high-risk group had a significantly higher risk of poor survival (log-rank P value = 2.80 e − 06 ; adjusted   hazard   ratio = 2.386 , 95% confidence interval: 1.607~3.543), a concordance index (C-index) of 0.714, and a Brier score of 0.184. The model performed well both in the 10-fold CV procedure and three independent cohorts from the Gene Expression Omnibus (GEO) repository. Conclusions. A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.