Adverse Outcome Pathway Development for Assessment of Lung Carcinogenicity by Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lung cancer, one of the most common and deadly forms of cancer, is in some cases associated with exposure to certain types of particles. With the rise of nanotechnology, there is concern that some engineered nanoparticles may be among such particles. In the absence of epidemiological evidence, assessment of nanoparticle carcinogenicity is currently performed on a time-consuming case-by-case basis, relying mainly on animal experiments. Non-animal alternatives exist, including a few validated cell-based methods accepted for regulatory risk assessment of nanoparticles. Furthermore, new approach methodologies (NAMs), focused on carcinogenic mechanisms and capable of handling the increasing numbers of nanoparticles, have been developed. However, such alternative methods are mainly applied as weight-of-evidence linked to generally required animal data, since challenges remain regarding interpretation of the results. These challenges may be more easily overcome by the novel Adverse Outcome Pathway (AOP) framework, which provides a basis for validation and uptake of alternative mechanism-focused methods in risk assessment. Here, we propose an AOP for lung cancer induced by nanosized foreign matter, anchored to a selection of 18 standardized methods and NAMs for in silico - and in vitro -based integrated assessment of lung carcinogenicity. The potential for further refinement of the AOP and its components is discussed in relation to available nanosafety knowledge and data. Overall, this perspective provides a basis for development of AOP-aligned alternative methods-based integrated testing strategies for assessment of nanoparticle-induced lung cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it