Comparative Pathology of Environmental Lung Disease: An Overview
Bibliographic record
Abstract
Environmental factors play a major role in a majority of lung diseases. Asthma, chronic obstructive pulmonary disease (COPD), lung cancer, and many interstitial lung diseases are influenced or caused by environmental factors. Animals and humans may respond differently to the same agent, and a study of the comparative pathology between the two is useful for optimizing animal models of environmental lung disease and for evaluating their predictive value in carcinogenicity studies. This overview describes the most common nonneoplastic pathologic pulmonary responses to inhaled environmental agents in the human and contrasts them with the responses observed in rats exposed to the same agents. We show both similarities and difference in response to the same agents; furthermore, both species have unique responses to some agents (for example, progressive massive fibrosis in the human and proliferative squamous lesions in the rat). Quantitative analysis of the grades of response to three environmental particulate dusts revealed differences between the 2 species at the cellular level. Specifically, acute intra-alveolar inflammation, alveolar epithelial hyperplasia, and alveolar lipoproteinosis were all greater in rats than in humans exposed to the same agents. These differences may account for differences between the 2 species in carcinogenic response to nonfibrous particulates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".