0
Systematic Review ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 1 ? Systematic review or meta-analysis. Synthesizes findings across many studies. Strongest evidence. Human Health Effects Sign in to save

Methods, models, mechanisms and metadata: Introducing the Nanotoxicology collection at F1000Research

F1000Research 2021 4 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Mary Gulumian, Penny Nymark, Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Penny Nymark, Philip Doganis, Iseult Lynch Mary Gulumian, Iseult Lynch Penny Nymark, Iseult Lynch Mary Gulumian, Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Tae Hyun Yoon, Diego Stéfani T. Martinez, Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Antreas Afantitis, Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Diego Stéfani T. Martinez, Tae Hyun Yoon, Iseult Lynch Antreas Afantitis, Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Antreas Afantitis, Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Antreas Afantitis, Mary Gulumian, Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Iseult Lynch Penny Nymark, Iseult Lynch Iseult Lynch

Summary

This editorial introduces the F1000Research Nanotoxicology collection, highlighting the interdisciplinary challenges in studying nanomaterial safety, including inconsistent reporting standards, difficulty reproducing results across laboratories, and gaps between materials characterization and biological testing. The collection aims to improve research quality through open peer review and standardized methods. These challenges directly parallel those facing microplastic toxicology, where inconsistent methods and reporting make it difficult to compare findings across studies.

Models

<ns3:p>Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with the development of applications. Nanotoxicology as a field spans materials synthesis and characterisation, assessment of fate and behaviour, exposure science, toxicology / ecotoxicology, molecular biology and toxicogenomics, epidemiology, safe and sustainable by design approaches, and chemoinformatics and nanoinformatics, thus requiring scientists to work collaboratively, often outside their core expertise area. This interdisciplinarity can lead to challenges in terms of interpretation and reporting, and calls for a platform for sharing of best-practice in nanotoxicology research. The F1000Research Nanotoxicology collection, introduced via this editorial, will provide a place to share accumulated best practice, via original research reports including no-effects studies, protocols and methods papers, software reports and living systematic reviews, which can be updated as new knowledge emerges or as the domain of applicability of the method, model or software is expanded. This editorial introduces the Nanotoxicology Collection in <ns3:italic>F1000Research</ns3:italic>. The aim of the collection is to provide an open access platform for nanotoxicology researchers, to support an improved culture of <ns3:ext-link xmlns:ns4="http://www.w3.org/1999/xlink" ext-link-type="uri" ns4:href="https://www.nature.com/articles/s41565-021-00911-6">data sharing</ns3:ext-link> and documentation of evolving protocols, biological and computational models, software tools and datasets, that can be applied and built upon to develop predictive models and move towards<ns3:italic> in silico </ns3:italic>nanotoxicology and nanoinformatics. Submissions will be assessed for fit to the collection and subjected to the F1000Research open peer review process.</ns3:p>

Sign in to start a discussion.

Share this paper