Induction of pulmonary antibodies against oxidized lipids in mice exposed to cigarette smoke
Bibliographic record
Abstract
BACKGROUND: Chronic cigarette smoke exposure is known to activate the adaptive immune system; however, the functional role of these processes is currently unknown. Given the role of oxidized lipids in driving innate inflammatory responses to cigarette smoke, we investigated whether an adaptive immune response against damaged lipids was induced following chronic cigarette smoke exposure. METHODS AND RESULTS: Using a well-established mouse model, we showed that cigarette smoke exposure led to a progressive increase in pulmonary antibodies against oxidized low-density lipoprotein (OxLDL). Functionally, we found that intranasal delivery of an antibody against oxidized phosphatidylcholine (anti-OxPC; clone E06) increased lipid and particle uptake by pulmonary macrophages without exacerbating cigarette smoke-induced neutrophilia. We also found that anti-OxPC treatment increased particle uptake following smoking cessation. Finally, the frequency of pulmonary macrophages with internalized particles was increased after prolonged smoke exposure, at which time lung anti-OxPC responses were highest. CONCLUSIONS: Altogether, this is the first report to demonstrate a non-pathogenic, and possibly protective, function of a newly identified autoantibody induced by chronic cigarette smoke exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".