COMPARATIVE PULMONARY TOXICITY OF VARIOUS SOLUBLE METALS FOUND IN URBAN PARTICULATE DUSTS
Bibliographic record
Abstract
The potential toxicity of an atmospheric dust sample EHC-93 has been attributed to the soluble fraction and, more specifically, to the zinc component. The concentration of Zn is the highest among the metals present in the soluble EHC-93 fraction. We now determine whether other metal components of this dust could cause similar lung injury if present at the same concentration as Zn (4.8 mg/g dust). Solutions of Zn, Cu, V, Ni, Fe, and Pb salts in 0.1 mL water were instilled to mouse lung and animals were killed at intervals up to 2 weeks later; each mouse received tritiated thymidine 1 hour before death. Solutions containing Zn and to a lesser degree Cu induced lung injury; in addition, increased numbers of alveolar macrophages and polymorphonuclear leukocytes were found in the lavage fluid, which also contained increased protein levels up to 1 week later. The magnitude of response was similar to that seen after administering EHC-93 dust at 1 mg in 0.1 mL water, whereas the response to other metal solutions containing Ni, Fe, Pb, and V was minimal. Morphologic evidence of lung injury and inflammation was also seen after EHC dust and the Zn or Cu solutions only. Reparative cell proliferation was measured after thymidine uptake and autoradiographs showed increased labeling of lung cells, particularly at 3 and 7 days. Labeling was confined to bronchiolar and type 2 alveolar epithelial cells, indicating previous epithelial cell necrosis in response to Zn or Cu. The results indicate that atmospheric contaminant metals Zn and Cu are most likely to cause lung injury and inflammation as compared to metals such as Ni, Fe, Pb, and V at the same concentrations. It appears that similar toxicity occurs when both redox (Cu) and nonredox (Zn) reactions are involved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".