The HLA-B27 Transgenic Rat, a Model of Spondyloarthritis, Has Decreased Bone Mineral Density and Increased RANKL to Osteoprotegerin mRNA Ratio
Bibliographic record
Abstract
OBJECTIVE: Bone metabolism in spondyloarthritis (SpA) is not well elucidated. We investigated alterations in bone in the HLA-B27 transgenic rat, a model of SpA. METHODS: Femur, tibia, and lumbar vertebral bodies of disease-prone HLA-B27 transgenic, healthy HLA-B7 transgenic, and nontransgenic control rats were used for bone histomorphometric and dual energy x-ray absorptiometry (DEXA) analysis. Serum levels of type I collagen C-telopeptides (CTX), N-terminal propeptide of type I procollagen (P1NP), and osteocalcin, as well as receptor activator of nuclear factor-kappa B ligand (RANKL) and osteoprotegerin (OPG), were measured. RNA was isolated from the bone tissue of the femura to analyze gene expression of RANKL, OPG, and osteocalcin. RESULTS: Histomorphometric analysis indicated a significant decrease in bone volume as well as trabecular number and thickness in the HLA-B27 rats. Trabecular separation was increased. Numbers of osteoblasts, osteoclasts, and osteoid volume were not altered significantly. The decrease in bone mineral density was confirmed using DEXA. Levels of RANKL mRNA were significantly increased in the bone tissue of HLA-B27 transgenic rats, resulting in an increased RANKL to OPG ratio. Osteocalcin mRNA expression was also significantly elevated in bone of HLA-B27 rats. Serum levels of CTX, RANKL, OPG, P1NP, and osteocalcin did not differ significantly. CONCLUSION: Our data indicate that, similarly to SpA in humans, HLA-B27 transgenic rats show a reduced bone mass, and suggest an involvement of the RANKL/OPG system in the mechanism of bone loss in this disease. This model may be adequate to study osteoporosis in SpA.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".