Smoking alters α1-antitrypsin in alveolar macrophages possibly promoting misfolding
Bibliographic record
Abstract
Background: The antiprotease and anti-inflammatory roles of a1-antitrypsin (AAT) are essential in protection of lung integrity. The PiZZ allele of AAT promotes misfolding and polymerization of the molecule that is retained in the endoplasmic reticulum (ER) resulting in low AAT serum levels. Alveolar macrophages (AM) produce AAT but their level of production in relation with AAT genotype and smoking has been scantily investigated. Aims: To investigate AAT expression in alveolar macrophages from patients with severe COPD, with or without AATD, and smoking and nonsmoking controls. Methods: AAT expression and localization were studied by immunohistochemistry and immunofluorescence in 10 explanted lungs with AATD, 26 with “usual” COPD, 17 smokers without COPD and 11 nonsmokers. Results: The percentage of AM expressing AAT (43;3-78% vs 50;3-76% vs 42;0-100 vs 56;2-97%) was similar in all groups. AAT expression correlated with lymphocytic lung inflammation (r=0.42,p=0.01). PAS staining revealed globular inclusions in AM, suggestive of AAT molecule aggregation in the endoplasmic reticulum, in COPD lungs with or without AATD (50%) and control smokers (80%) but not in nonsmokers (p=0.01). Immunofluorescence showed low-intensity diffuse AAT staining in nonsmokers but globular staining in smokers, that was coexpressed with the ER marker calreticulin, confirming the PAS pattern. Conclusions: Alveolar macrophages in the lungs of non-smoking, smoking and COPD subjects express AAT. In AATD and smokers, with and without COPD, a globular form of AAT, possibly misfolded or polymerized, is seen retained in endoplasmic reticulum, suggesting that smoking alters the AAT molecule in AM promoting misfolding.
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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 | 0.000 | 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".