Chronic smoke exposure induces rheumatoid factor and anti‐heat shock protein 70 autoantibodies in susceptible mice and humans with lung disease
Bibliographic record
Abstract
The impact of cigarette smoke (CS), a risk factor for rheumatoid arthritis (RA), on sauto-antibody production was studied in humans and mice with and without chronic lung disease (LD). Rheumatoid factor (RF), anti-cyclic citrullinated peptides (CCPs), and anti-HSP70 autoantibodies were measured in several mouse strains and in cohorts of smokers and nonsmokers with and without autoimmune disease. Chronic smoking-induced RFs in AKR/J mice, which are most susceptible to LD. RFs were identified in human smokers, preferentially in those with LD. Anti-HSP70 auto-antibodies were identified in CS-exposed AKR/J mice but not in ambient air exposed AKR/J controls. Whereas inflammation could induce anti-HSP70 IgM, smoke exposure promoted the switch to anti-HSP70 IgG autoantibodies. Elevated anti-CCP autoantibodies were not detected in CS-exposed mice or smokers. AKR/J splenocytes stimulated in vitro by immune complexes (ICs) of HSP70/anti-HSP70 antibodies produced RFs. The CD91 scavenger pathway was required as anti-CD91 blocked the HSP70-IC-induced RF response. Blocking Toll-like receptors did not influence the HSP70-IC-induced RFs. These studies identify both anti-HSP70 and RFs as serological markers of smoke-related LD in humans and mice. Identification of these autoantibodies could suggest a common environmental insult, namely CS, in a number of different disease settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".