Expert consensus on an in vitro approach to assess pulmonary fibrogenic potential of aerosolized nanomaterials
Bibliographic record
Abstract
The increasing use of multi-walled carbon nanotubes (MWCNTs) in consumer products and their potential to induce adverse lung effects following inhalation has lead to much interest in better understanding the hazard associated with these nanomaterials (NMs). While the current regulatory requirement for substances of concern, such as MWCNTs, in many jurisdictions is a 90-day rodent inhalation test, the monetary, ethical, and scientific concerns associated with this test led an international expert group to convene in Washington, DC, USA, to discuss alternative approaches to evaluate the inhalation toxicity of MWCNTs. Pulmonary fibrosis was identified as a key adverse outcome linked to MWCNT exposure, and recommendations were made on the design of an in vitro assay that is predictive of the fibrotic potential of MWCNTs. While fibrosis takes weeks or months to develop in vivo, an in vitro test system may more rapidly predict fibrogenic potential by monitoring pro-fibrotic mediators (e.g., cytokines and growth factors). Therefore, the workshop discussions focused on the necessary specifications related to the development and evaluation of such an in vitro system. Recommendations were made for designing a system using lung-relevant cells co-cultured at the air-liquid interface to assess the pro-fibrogenic potential of aerosolized MWCNTs, while considering human-relevant dosimetry and NM life cycle transformations. The workshop discussions provided the fundamental design components of an air-liquid interface in vitro test system that will be subsequently expanded to the development of an alternative testing strategy to predict pulmonary toxicity and to generate data that will enable effective risk assessment of NMs.
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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 | 0.077 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".