Genotoxicity of nanomaterials: Refining strategies and tests for hazard identification
Bibliographic record
Abstract
A workshop addressing strategies for the genotoxicity assessment of nanomaterials (NMs) was held on October 23, 2010 in Fort Worth Texas, USA. The workshop was organized by the Environmental Mutagen Society and the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute. The workshop was attended by more than 80 participants from academia, regulatory agencies, and industry from North America, Europe and Japan. A plenary session featured summaries of the current status and issues related to the testing of NMs for genotoxic properties, as well as an update on international activities and regulatory approaches. This was followed by breakout sessions and a plenary session devoted to independent discussions of in vitro assays, in vivo assays, and the need for new assays or new approaches to develop a testing strategy for NMs. Each of the standard assays was critiqued as a resource for evaluation of NMs, and it became apparent that none was appropriate without special considerations or modifications. The need for nanospecific positive controls was questioned, as was the utility of bacterial assays. The latter was thought to increase the importance of including mammalian cell gene mutation assays into the test battery. For in-vivo testing, to inform the selection of appropriate tests or protocols, it was suggested to run repeated dose studies first to learn about disposition, potential accumulation, and possible tissue damage. It was acknowledged that mechanisms may be at play that a standard genotoxicity battery may not be able to capture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".