21st Century Tools for Nanotoxicology: Transcriptomic Biomarker Panel and Precision‐Cut Lung Slice Organ Mimic System for the Assessment of Nanomaterial‐Induced Lung Fibrosis
Bibliographic record
Abstract
There is an urgent need for reliable toxicity assays to support the human health risk assessment of an ever increasing number of engineered nanomaterials (ENMs). Animal testing is not a suitable option for ENMs. Sensitive in vitro models and mechanism-based targeted in vitro assays that enable accurate prediction of in vivo responses are not yet available. In this proof-of-principle study, publicly available mouse lung transcriptomics data from studies investigating xenobiotic-induced lung diseases are used and a 17-gene biomarker panel (PFS17) applicable to the assessment of lung fibrosis is developed. The PFS17 is validated using a limited number of in vivo mouse lung transcriptomics datasets from studies investigating ENM-induced responses. In addition, an ex vivo precision-cut lung slice (PCLS) model is optimized for screening of potentially inflammogenic and pro-fibrotic ENMs. Using bleomycin and a multiwalled carbon nanotube, the practical application of the PCLS method as a sensitive alternative to whole animal tests to screen ENMs that may potentially induce inhalation toxicity is shown. Conditional to further optimization and validation, it is established that a combination of PFS17 and the ex vivo PCLS method will serve as a robust and sensitive approach to assess lung inflammation and fibrosis induced by ENMs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".