Characterization of M1 and M2 macrophages in human lung tissue
Bibliographic record
Abstract
Background. Alveolar macrophages (AM) in experimental models can express a proinflammatory M1 or an antiinflammatory M2 phenotype. The presence and function of these phenotypes in human disease have not been established. Aim. To study if the M1 and M2 phenotypes of AM are expressed in humans, in particular in lung tissue of nonsmokers and smokers with and without COPD. Methods. Alveolar macrophages were immunostained in sequential slides for M1 (iNOS) and M2 (CD206) in lungs of 11 severe COPD, 13 smokers without COPD and 11 nonsmokers. M1 and M2 AM were counted and expressed as percentage of total AM. Results. In COPD the proportion of M1 [53(16-81)%] and M2 [76(40-98)%] was higher than in nonsmokers: M1 [24(11-44)%] and M2 [44(0-88)%], p<0.05 for both. The proportion of M1 was also higher in smokers without COPD [52(31-81)%], than in nonsmokers [24(11-44)%] (p=0.009). In COPD there were more M2 than M1 AM (p=0.03). The sum of the percentage of AM expressing M1 and M2 markers exceeded 100% in most of COPD subjects [132(85-159)%] and smokers without COPD [124(87-144)%] suggesting that a high proportion of AM expressed both M1 and M2 markers. From these data we calculated that the alveolar macrophages M0 (no M1, no M2) decreased from a minimum of 32% in nonsmokers to a minimum of 15% in smokers with COPD (p=0.004). Conclusions. Alveolar macrophages can be present as M0, M1 and M2 in normal and diseased lungs. Nonsmokers have a high proportion of M0 macrophages that decreases markedly in COPD. With the development of the chronic inflammation characteristic of COPD alveolar macrophages express both M1 and M2 markers in high proportion, often simultaneously.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".