Abnormal M1/M2 macrophage phenotype profiles in the small airway wall and lumen in smokers and chronic obstructive pulmonary disease (COPD)
Bibliographic record
Abstract
We explore potential dysregulation of macrophage phenotypes in COPD pathogenesis through integrated study of human small airway tissue, bronchoalveolar lavage (BAL) and an experimental murine model of COPD. We evaluated human airway tissue and BAL from healthy controls, normal lung function smokers (NLFS), and COPD subjects. Both small airways and BAL cells were immunohistochemically stained with anti-CD68 for total macrophages and with anti-CD163 for M2, and anti-iNOS for M1 macrophages. Multiplex ELISA measured BAL cytokines. Comparable cigarette smoke-induced experimental COPD mouse model was assessed for relevant mRNA profiles. We found an increase in pro-inflammatory M1s in the small airways of NLFS and COPD compared to controls with a reciprocal decrease in M2 macrophages, which remained unchanged among pathological groups. However, luminal macrophages showed a dominant M2 phenotype in both NLFS and COPD subjects. BAL cytokine skewed towards an M2 profile with increase in CCL22, IL-4, IL-13, and IL-10 in both NLFS and COPDs. The mouse-model of COPD showed similar increase in mRNA for M2 markers. Our finding suggests abnormal macrophage switching in both mucosal and luminal areas of COPD patients, that strongly associated with cytokine balance. There may be potential for beneficial therapeutic cytokine manipulation of macrophage phenotypes in COPD.
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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".