Mass or total surface area with aerosol size distribution as exposure metrics for inflammatory, cytotoxic and oxidative lung responses in rats exposed to titanium dioxide nanoparticles
Bibliographic record
Abstract
on rat lung inflammatory, cytotoxicity, and oxidative stress responses. We found a significant positive correlation ( r = 0.98, p < 0.01) with the inflammatory reaction, measured by the number of neutrophils and the mass concentration when considering all six (SA + LA) aerosols. This correlation was similar ( r = 0.87) for total surface area. Regarding cytotoxicity and oxidative stress responses, measured by lactate dehydrogenase and 8-isoprostane, respectively, and mass or total surface area as an exposure metric, we observed significant positive correlations only with SA aerosols for both the mass concentration and size distribution ( r > 0.91, p < 0.01), as well as for the total surface area ( r > 0.97, p < 0.01). These data show that mass or total surface area concentrations alone are insufficient to adequately predict oxidant and cytotoxic pulmonary effects. Overall, our study indicates that considering NP size distribution along with mass or total surface area concentrations contributes to a more mechanistic discrimination of pulmonary responses to NP exposure.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".