ArticleParticle and fibre toxicology2023
A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data.
Article in Particle and fibre toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- Review
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Molecular Representation and Closed-Loop Validation for Toxicity Assessment of Organic Compounds in Ambient Air PMEnvironmental science & technology · 2026Article
- Machine learning-guided Nano-QSAR modeling predicts HepaRG cell membrane toxicity of engineered nanoparticles with mechanistic insights.Cell biology and toxicology · 2026Article
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
- Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning.ACS nano · 2025Article
- The nano-paradox: addressing nanotoxicity for sustainable agriculture, circular economy and SDGs.Journal of nanobiotechnology · 2025Review
- Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-informed Machine Learning.Research square · 2025Article
- Orchestrating cancer therapy: Recent advances in nanoplatforms harmonize immunotherapy with multifaceted treatments.Materials today. Bio · 2025Review
- AI-based nanotoxicity data extraction and prediction of nanotoxicity.Computational and structural biotechnology journal · 2025Article
- Techniques and Instruments for Assessing and Reducing Risk of Exposure to Nanomaterials in Construction, Focusing on Fire-Resistant Insulation Panels Containing Nanoclay.Nanomaterials (Basel, Switzerland) · 2024Article
- Opportunities and Challenges for Inhalable Nanomedicine Formulations in Respiratory Diseases: A Review.International journal of nanomedicine · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles.
resultsTree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R
conclusionsThe proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.