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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".