Decoding the inflammatory-osteogenic axis in ankylosing spondylitis: mechanisms, dysregulation, and emerging therapeutic interventions
Bibliographic record
Abstract
Ankylosing spondylitis (AS) is a chronic autoimmune disorder that primarily affects young people. Although genetic and environmental factors have been implicated in the pathogenesis of AS, the etiology of this condition remains unclear. Observations indicate that individuals possessing the human leukocyte antigen (HLA)-B27 allele exhibit elevated risk factors, as any mutation within this gene could potentially result in the development of AS in the future. However, it is interesting to note that many AS patients do not carry this gene, inferring the involvement of other genetic and nongenetic factors in the development of the disease. As the exact mechanisms remain unknown, no target-specific treatments exist to cure AS. Nonetheless, some treatment regimens have been devised to alleviate AS symptoms. This review thoroughly examines the molecular mechanisms implicated in AS, encompassing insights into the significance of pivotal biomarkers, such as extracellular matrix metabolites, immune cell dynamics, gut microbiota interactions, the Wnt signaling pathway, and its inhibitors. Furthermore, a thorough evaluation of the different mouse models used in AS research has been reviewed, which is crucial for understanding disease pathways and assessing treatment methods. In addition, significant progress in developing effective treatment strategies for AS, along with drugs available for treatment and ongoing clinical trials, has been summarized. A comprehensive understanding of experimental mouse models, along with insights into molecular mechanisms and biomarkers for AS, could aid researchers and physicians in discovering new treatment strategies for this challenging condition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".