ArticleToxics2024
Prediction of Dynamic Toxicity of Nanoparticles Using Machine Learning.
Article in Toxics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Review
- A Multimodal Generative AI Framework for Predicting the Toxicity of Nanoparticles.Nanomaterials (Basel, Switzerland) · 2026Article
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Machine Learning on Systematically Curated Data Reveals Key Determinants of Magnetic Hyperthermia Performance.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- The landscape of nanomedical clinical trials.Nano today · 2026Article
- Enhancing the Safety of Nanoparticles in Medicine: Highlights of Progress and Critical Objectives.International journal of nanomedicine · 2026Review
- Artificial Intelligence in the Design and Development of Nanoparticle Drug Delivery Systems: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 2026Review
- Reproducibility roadblocks and standardization in carbon dot synthesis: a critical review of current practices, challenges, and future directions.Mikrochimica acta · 2025Review
- Article
- Extracellular Vesicles and Nanoparticles in Regenerative and Personalised Medicine: Diagnostic and Therapeutic Roles-A Narrative Review.Pharmaceutics · 2025Review
- AI-based nanotoxicity data extraction and prediction of nanotoxicity.Computational and structural biotechnology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Predicting the toxicity of nanoparticles plays an important role in biomedical nanotechnologies, in particular in the creation of new drugs. Safety analysis of nanoparticles can identify potentially harmful effects on living organisms and the environment. Advanced machine learning models are used to predict the toxicity of nanoparticles in a nutrient solution. In this article, we performed a comparative analysis of the current state of research in the field of nanoparticle toxicity analysis using machine learning methods; we trained a regression model for predicting the quantitative toxicity of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of MSE = 2.19 and RMSE = 1.48; we trained a multi-class classification model for predicting the toxicity class of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of Accuracy = 0.9756, Recall = 0.9623, F1-Score = 0.9640, and Log Loss = 0.1855. As a result of the analysis, we concluded the good predictive ability of the trained models. The optimal dosages for the nanoparticles under study were determined as follows: ZnO = 9.5 × 10
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What OpenQuestion holds
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 OpenQuestion graph.