Kartogenin treatment prevented joint degeneration in a rodent model of osteoarthritis: A pilot study
Bibliographic record
Abstract
ABSTRACT Osteoarthritis (OA) is a major degenerative joint disease characterized by progressive loss of articular cartilage, synovitis, subchondral bone changes, and osteophyte formation. Currently there is no treatment for OA except temporary pain relief and end‐stage joint replacement surgery. We performed a pilot study to determine the effect of kartogenin (KGN, a small molecule) on both cartilage and subchondral bone in a rat model of OA using multimodal imaging techniques. OA was induced in rats (OA and KGN treatment group) by anterior cruciate ligament transection (ACLT) surgery in the right knee joint. Sham surgery was performed on the right knee joint of control group rats. KGN group rats received weekly intra‐articular injection of 125 μM KGN 1 week after surgery until week 12. All rats underwent in vivo magnetic resonance imaging (MRI) at 3, 6, and 12 weeks after surgery. Quantitative MR relaxation measures (T1ρ and T2) were determined to evaluate changes in articular cartilage. Cartilage and bone turnover markers (COMP and CTX‐I) were determined at baseline, 3, 6, and 12 weeks. Animals were sacrificed at week 12 and the knee joints were removed for micro‐computed tomography (micro‐CT) and histology. KGN treatment significantly lowered the T1ρ and T2 relaxation times indicating decreased cartilage degradation. KGN treatment significantly decreased COMP and CTX‐I levels indicating decreased cartilage and bone turnover rate. KGN treatment also prevented subchondral bone changes in the ACLT rat model of OA. Thus, kartogenin is a potential drug to prevent joint deterioration in post‐traumatic OA. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:1780–1789, 2016.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".