Complexity of enthesitis and new bone formation in ankylosing spondylitis: current understanding of the immunopathology and therapeutic approaches
Bibliographic record
Abstract
Despite increasing availability of treatments for spondyloarthritis (SpA) including tumour necrosis factor (TNF) and interleukin-17 (IL-17) inhibitors, there is no established treatment that abates new bone formation (NBF) in ankylosing spondylitis (AS), a subset of SpA. Recent research on TNF has revealed the increased level of transmembrane TNF in the joint tissue of SpA patients compared to that of rheumatoid arthritis patients, which appears to facilitate TNF-driven osteo-proliferative changes in AS. In addition, there is considerable interest in the central role of IL-23/IL-17 axis in type 3 immunity and the therapeutic potential of blocking this axis to ameliorate enthesitis and NBF in AS. AS immunopathology involves a variety of immune cells, including both innate and adoptive immune cells, to orchestrate the immune response driving type 3 immunity. In response to external stimuli of inflammatory cytokines, local osteo-chondral progenitor cells activate intra-cellular anabolic molecules and signals involving hedgehog, bone morphogenetic proteins, receptor activator of nuclear factor kappa-B ligand, and Wnt pathways to promote NBF in AS. Here, we provide an overview of the current immunopathology and future directions for the treatment of enthesitis and NBF associated with AS.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".