Expression of Peroxisome Proliferator-activated Receptors α, β, γ, and H- and L-Prostaglandin D Synthase During Osteoarthritis in the Spontaneous Hartley Guinea Pig and Experimental Dog Models
Bibliographic record
Abstract
OBJECTIVE: To investigate the expression of peroxisome proliferator-activated receptors (PPAR) α, β, and γ, and hematopoietic and lipocalin-type prostaglandin D synthase (H- and L-PGDS) over the course of osteoarthritis (OA) in the spontaneous Hartley guinea pig and the anterior cruciate ligament transection dog models. METHODS: Guinea pigs were sacrificed at 2 (control group), 4, 8, and 12 months of age (n = 5 per group). Non-operated (control) and operated dogs were sacrificed at 4, 8, and 12 weeks postsurgery. Cartilage was evaluated histologically using the Osteoarthritis Research Society International (OARSI) guidelines. The expression of PPAR-α, β, γ, and H- and L-PGDS was evaluated by real-time PCR and immunohistochemistry. The nonparametric Spearman test was used for correlation analysis. RESULTS: PPAR-α, β, and γ were detected in medial tibial plateau from control animals in both the spontaneous and surgical models. Levels of PPAR-α and β did not change over the course of OA, whereas PPAR-γ levels decreased during progression of disease. We also observed that the expression of H-PGDS remained unchanged, whereas L-PGDS increased over the course of OA. PPAR-γ levels correlated negatively, whereas L-PGDS levels correlated positively, with the histological score of OA. CONCLUSION: The level of PPAR-γ decreased, whereas level of L-PGDS increased during the progression of OA. These data suggest that reduced expression of PPAR-γ may contribute to the pathogenesis of OA, whereas enhanced expression of L-PGDS may be part of a reparative process.
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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) | 0.000 | 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) | 0.002 | 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".