Macrophage Migration Inhibitory Factor Induces Inflammation and Predicts Spinal Progression in Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of macrophage migration inhibitory factor (MIF) in the pathogenesis of ankylosing spondylitis (AS). METHODS: Patients who met the modified New York criteria for AS were recruited for the study. Healthy volunteers, rheumatoid arthritis patients, and osteoarthritis patients were included as controls. Based on the annual rate of increase in modified Stoke AS Spine Score (mSASSS), AS patients were classified as progressors or nonprogressors. MIF levels in serum and synovial fluid were quantitated by enzyme-linked immunosorbent assay. Predictors of AS progression were evaluated using logistic regression analysis. Immunohistochemical analysis of ileal tissue was performed to identify MIF-producing cells. Flow cytometry was used to identify MIF-producing subsets, expression patterns of the MIF receptor (CD74), and MIF-induced tumor necrosis factor (TNF) production in the peripheral blood. MIF-induced mineralization of osteoblast cells (SaOS-2) was analyzed by alizarin red S staining, and Western blotting was used to quantify active β-catenin levels. RESULTS: Baseline serum MIF levels were significantly elevated in AS patients compared to healthy controls and were found to independently predict AS progression. MIF levels were higher in the synovial fluid of AS patients, and MIF-producing macrophages and Paneth cells were enriched in their gut. MIF induced TNF production in monocytes, activated β-catenin in osteoblasts, and promoted the mineralization of osteoblasts. CONCLUSION: Our findings indicate an unexplored pathogenic role of MIF in AS and a link between inflammation and new bone formation.
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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 | 0.001 |
| 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) | 0.001 | 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".