Iron in airway macrophages and infective exacerbations of chronic obstructive pulmonary disease
Bibliographic record
Abstract
BACKGROUND: Excess pulmonary iron has been implicated in the pathogenesis of lung disease, including asthma and COPD. An association between higher iron content in sputum macrophages and infective exacerbations of COPD has previously been demonstrated. OBJECTIVES: To assess the mechanisms of pulmonary macrophage iron sequestration, test the effect of macrophage iron-loading on cellular immune function, and prospectively determine if sputum hemosiderin index can predict infectious exacerbations of COPD. METHODS: Intra- and extracellular iron was measured in cell-line-derived and in freshly isolated sputum macrophages under various experimental conditions including treatment with exogenous IL-6 and hepcidin. Bacterial uptake and killing were compared in the presence or absence of iron-loading. A prospective cohort of COPD patients with defined sputum hemosiderin indices were monitored to determine the annual rate of severe infectious exacerbations. RESULTS: Gene expression studies suggest that airway macrophages have the requisite apparatus of the hepcidin-ferroportin axis. IL-6 and hepcidin play roles in pulmonary iron sequestration, though IL-6 appears to exert its effect via a hepcidin-independent mechanism. Iron-loaded macrophages had reduced uptake of COPD-relevant organisms and were associated with higher growth rates. Infectious exacerbations were predicted by sputum hemosiderin index (β = 0.035, p = 0.035). CONCLUSIONS: We demonstrate in-vitro and population-level evidence that excess iron in pulmonary macrophages may contribute to recurrent airway infection in COPD. Specifically, IL-6-dependent iron sequestration by sputum macrophages may result in immune cell dysfunction and ultimately lead to increased frequency of infective exacerbation.
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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) | 0.000 | 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) | 0.001 | 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".