Where there’s smoke, there’s fire: insights from murine models on the effect of cigarette smoke in rheumatoid arthritis development
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by joint inflammation and damage, leading to disability and pain. The etiology of RA is undefined but considered multifactorial, as interactions between genetics and environmental factors lead to the generation of autoantibodies that target synovial joints. Smoking is a well-established and widely studied risk factor for RA development and is associated with a reduced response to treatments and poor clinical outcomes. Murine models of inflammatory arthritis have provided many insights into the pathogenesis of RA and have recently been used to explore the relationship between cigarette smoking and RA. In this review, we comprehensively appraise the current literature investigating cigarette smoke exposure in murine models of inflammatory arthritis, focused on RA. The current literature indicates that the influence of smoke exposure on molecular and disease outcomes depends on the timepoint of exposure and genetic background of the mice. Further, dose-dependent increases in disease manifestations reproduce human clinical data that the intensity of smoking is linked to disease but demosntrate that there may be a plateau effect. Finally, we consolidate mechanistic findings to describe a potential mechanism through which cigarette smoke exacerbates murine arthritis. Understanding how these factors, genetics, timing, and intensity of exposure modulate response to CS in inflammatory arthritis models may lead to better drug development and personalized treatment strategies, ultimately improving outcomes for RA patients with a smoking history.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".