Critical review of the current and future challenges associated with advanced<i>in vitro</i>systems towards the study of nanoparticle (secondary) genotoxicity
Bibliographic record
Abstract
With the need to understand the potential biological impact of the plethora of nanoparticles (NPs) being manufactured for a wide range of potential human applications, due to their inevitable human exposure, research activities in the field of NP toxicology has grown exponentially over the last decade. Whilst such increased research efforts have elucidated an increasingly significant knowledge base pertaining to the potential human health hazard posed by NPs, understanding regarding the possibility for NPs to elicit genotoxicity is limited. In vivo models are unable to adequately discriminate between the specific modes of action associated with the onset of genotoxicity. Additionally, in line with the recent European directives, there is an inherent need to move away from invasive animal testing strategies. Thus, in vitro systems are an important tool for expanding our mechanistic insight into NP genotoxicity. Yet uncertainty remains concerning their validity and specificity for this purpose due to the unique challenges presented when correlating NP behaviour in vitro and in vivo This review therefore highlights the current state of the art in advanced in vitro systems and their specific advantages and disadvantages from a NP genotoxicity testing perspective. Key indicators will be given related to how these systems might be used or improved to enhance understanding of NP genotoxicity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".