Interplay between cigarette smoking and pulmonary reverse lipid transport
Bibliographic record
Abstract
Reverse lipid transport is critical to maintain homeostasis. Smoking causes lipid accumulation in macrophages, therefore suggesting suboptimal reverse lipid transport mechanisms. In this study, we investigated the interplay between smoking and reverse lipid transport and the consequences on smoking-induced lung and peripheral alterations. To investigate the relationship between smoking and reverse lipid transport, we used a clinical lung gene expression dataset and a mouse model of cigarette smoke exposure. We also used ApoA-1 −/− mice, with reduced reverse lipid transport capacity, and a recombinant ApoA-1 Milano/phospholipid complex (MDCO-216) to boost reverse lipid transport. Cellular and functional analyses were performed on the lungs and impact on body composition was also assessed. Smoking affects pulmonary expression of abca1 , abcg1 , apoe and scarb1 in both mice and humans, key genes involved in reverse lipid transport. In mice, the capacity of bronchoalveolar lavage fluid and serum to stimulate cholesterol efflux in macrophages was increased after a single exposure to cigarette smoke. ApoA-1 −/− mice showed increased lung neutrophilia, larger macrophages and greater loss in lean mass in response to smoking, whereas treatment with MDCO-216 reduced the size of macrophages and increased the lean mass of mice exposed to cigarette smoke. Altogether, this study shows a functional interaction between smoking and reverse lipid transport, and opens new avenues for better understanding the link between metabolic and pulmonary diseases related to smoking.
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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 | 0.001 | 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".