Innate immunity drives pathogenesis of rheumatoid arthritis
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease affecting ∼1% of the general population. This disease is characterized by persistent articular inflammation and joint damage driven by the proliferating synovial tissue fibroblasts as well as neutrophil, monocyte and lymphocyte trafficking into the synovium. The factors leading to RA pathogenesis remain poorly elucidated although genetic and environmental factors have been proposed to be the main contributors to RA. The majority of the early studies focused on the role of lymphocytes and adaptive immune responses in RA. However, in the past two decades, emerging studies showed that the innate immune system plays a critical role in the onset and progression of RA pathogenesis. Various innate immune cells including monocytes, macrophages and dendritic cells are involved in inflammatory responses seen in RA patients as well as in driving the activation of the adaptive immune system, which plays a major role in the later stages of the disease. Here we focus the discussion on the role of different innate immune cells and components in initiation and progression of RA. New therapeutic approaches targeting different inflammatory pathways and innate immune cells will be highlighted here. Recent emergence and the significant roles of innate lymphoid cells and inflammasomes will be also discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".