ArticleComputational and structural biotechnology journal2025
AI-based nanotoxicity data extraction and prediction of nanotoxicity.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Nanohydroxyapatite composites' toxicity and biosafety for safe clinical translation.Nanoscale advances · 2026Review
- Biologically Mediated Nanoparticle Synthesis as a Potential Green Strategy: Principles, Methods, and Pharmaceutical Applications.Pharmaceutics · 2026Review
- New approach methodologies for next-generation risk assessment of nanomaterials and nano-enabled products.Nano convergence · 2026Review
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- Metal-based nanodrugs for cancer immunotherapy: smart nanosystems, immunomodulatory mechanisms, and translational perspectives.Frontiers in chemistry · 2026Review
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among toxicity assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and in vivo assays, which involve high costs and ethical concerns. As a result, the qualitative and quantitative importance of data is now widely recognized. However, collecting large, high-quality data is both time-consuming and labor-intensive. Artificial intelligence (AI)-based data extraction techniques hold significant potential for extracting and organizing information from unstructured text. However, the use of large language models (LLMs) and prompt engineering for nanotoxicity data extraction has not been widely studied. In this study, we developed an AI-based automated data extraction pipeline to facilitate efficient data collection. The automation process was implemented using Python-based LangChain. We used 216 nanotoxicity research articles as training data to refine prompts and evaluate LLM performance. Subsequently, the most suitable LLM with refined prompts was used to extract test data, from 605 research articles. As a result, data extraction performance on training data achieved F1
Indexed as
Identifiers
What Socratic 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 Socratic graph.