Can CD200R1 Agonists Slow the Progression of Osteoarthritis Secondary to Injury?
Bibliographic record
Abstract
Post-traumatic knee osteoarthritis is characterized by cartilage degeneration, subchondral bone remodeling, osteophyte formation, and synovial changes. Therapeutic targeting of inflammatory activity in the knee immediately post injury may alter the course of osteoarthritis development. This study aimed to determine whether CD200R1 agonists, namely the protein therapeutic CD200Fc or the synthetic DNA aptamer CCS13, both known to act as anti-inflammatory agents, are able to delay the pathogenesis of injury-associated knee osteoarthritis in a murine model. Ten week old male C57BL/6 mice were randomized and surgical destabilization of the medial meniscus (DMM) to induce knee arthritis or sham surgery as a control were performed. CCS13 was evaluated as a therapeutic treatment along with CD200Fc and a phosphate-buffered saline vehicle control. Oligonucleotides were injected intra-articularly beginning one week after surgery, with a total of six injections administered prior to sacrifice at 12 weeks post-surgery. Histopathological assessment was used as the primary outcome measure to assess cartilage and synovial changes, while µCT imaging was used to compare the changes to the subchondral bone between untreated and treated arthritic groups. We did not find any attenuation of cartilage degeneration or synovitis in DMM mice with CD200Fc or CCS13 at 12 weeks post-surgery, nor stereological differences in the properties of subchondral bone. The use of CD200R1 agonists to blunt the inflammatory response in the knee are insufficient to prevent disease progression in the mouse DMM model of OA without anatomical restoration of the normal joint biomechanics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".