Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles.

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by (less than 0.40).

Untargeted Metabolic Pathway Analysis as an Effective Strategy to Connect Various Nanoparticle Properties to Nanoparticle-Induced Ecotoxicity.

Elucidation of the relationships between nanoparticle properties and ecotoxicity is a fundamental issue for environmental applications and risk assessment of nanoparticles. However, effective strategies to connect the various properties of nanoparticles with their ecotoxicity remain largely unavailable. Herein, an untargeted metabolic pathway analysis was employed to investigate the environmental risk posed by 10 typical nanoparticles (AgNPs, CuNPs, FeNPs, ZnONPs, SiONPs, TiONPs, GO, GOQDs, SWCNTs, and C) to rice (a staple food for half of the world's population).

Screening Priority Factors Determining and Predicting the Reproductive Toxicity of Various Nanoparticles.

Due to the numerous factors (e.g., nanoparticle [NP] properties and experimental conditions) influencing nanotoxicity, it is difficult to identify the priority factors dominating nanotoxicity.

Screening Small Metabolites from Cells as Multifunctional Coatings Simultaneously Improves Nanomaterial Biocompatibility and Functionality.

Currently, nanomaterials face a dilemma due to their advantageous properties and potential risks to human health. Here, a strategy to improve both nanomaterial biocompatibility and functionality is established by screening small metabolites from cells as nanomaterial coatings. A metabolomics analysis of cells exposed to nanosilver (nAg) integrates volcano plots (-tests and fold change analysis), partial least squares-discriminant analysis (PLS-DA), and significance analysis of microarrays (SAM) and identifies six metabolites (l-aspartic acid, l-malic acid, myoinositol, d-sorbitol, citric acid, and l-cysteine).