The Impact of Carbon Nanotube Properties on Lung Pathologies and Atherosclerosis Through Acute Inflammation: a New AOP‐Anchored <i>in Silico</i> NAM
Bibliographic record
Abstract
Abstract In this study, a previously developed approach for creating a quantitative structure‐activity relationship model anchored in an Adverse Outcome Pathway framework (AOP‐anchored Nano‐QSAR) is employed to develop a novel model capable of predicting transcriptomic responses triggered by the inhalation of multiwalled carbon nanotubes (MWCNTs). The acute phase response (AR) signaling pathway, which plays a crucial role in neutrophil influx and initiates the acute immune response is focused. This process involves recruiting pro‐inflammatory cells into the lungs and can lead to lung fibrosis, as outlined in AOP33, or atherosclerosis, as per AOP237. To establish the relationship between the structural properties of a set of MWCNTs and the transcriptional benchmark dose level (BMDLAR) response of genes associated with the acute phase response signaling pathway, the locally weighted kernel linear regression algorithm is used. These findings emphasize the critical role of the aspect ratio and specific surface area of MWCNTs in initiating acute inflammation and, subsequently, lung pathologies and atherosclerosis through the inflammatory and acute phase response signaling pathways. This newly developed data‐driven model extends the repertoire of transcriptomic‐based, AOP‐informed Nano‐QSAR models, potentially serving as an in silico new approach methodology (NAM) to support the MWCNTs’ safety assessment based on the weight of evidence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".