Iron Loading Makes a Nonfibrogenic Model Air Pollutant Particle Fibrogenic In Rat Tracheal Explants
Bibliographic record
Abstract
To examine the potential role of particle iron in fibrogenicity, we loaded nonfibrogenic fine (0.12micro) TiO(2) with increasing amounts of Fe(II)-Fe(III) chloride. Dusts were applied to rat tracheal explants, which were maintained in air organ culture for 1 wk. Iron-loaded dust increased procollagen gene expression and tissue hydroxyproline. The active oxygen species (AOS) scavenger tetramethylthiourea prevented these effects. Iron loading caused nuclear factor (NF)-kappaB activation, decreased levels of total IkappaBalpha, but relatively increased levels of both IkappaBalpha-phosphoserine 32/36 and IkappaBalpha-phosphotyrosine. A citrate extract of iron-loaded dust increased procollagen expression. Gel shift using a probe consisting of the NF-kappaB consensus sequence from the prolyl-4-hydroxylase promoter and adjacent bases showed increased nuclear binding, and RT-PCR examination showed increased prolyl-hydroxylase alpha-chain gene expression after iron loading. We conclude that addition of surface iron can convert a nonreactive model air pollutant particle into a fibrogenic particle via AOS- and NF-kappaB-dependent pathways, probably through two different NF-kappaB activation pathways in two different anatomic compartments. This process may proceed in vivo through iron extracted from the dust into the cytoplasm. NF-kappaB activation may directly increase expression of prolyl hydroxylase, an enzyme involved in collagen synthesis. These findings suggest that air pollutant particles containing significant quantities of transition metals may produce airway wall fibrosis and lead to chronic obstructive pulmonary disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".