Allogeneic Bone Marrow Transplant from MRL/MpJ Super-Healer Mice Does Not Improve Articular Cartilage Repair in the C57Bl/6 Strain
Bibliographic record
Abstract
BACKGROUND: Articular cartilage has been the focus of multiple strategies to improve its regenerative/ repair capacity. The Murphy Roths Large (MRL/MpJ) "super-healer" mouse demonstrates an unusual enhanced regenerative capacity in many tissues and provides an opportunity to further study endogenous cartilage repair. The objective of this study was to test whether the super-healer phenotype could be transferred from MRL/MpJ to non-healer C57Bl/6 mice by allogeneic bone marrow transplant. METHODOLOGY: The healing of 2mm ear punches and full thickness cartilage defects was measured 4 and 8 weeks after injury in control C57Bl/6 and MRL/MpJ "super-healer" mice, and in radiation chimeras reconstituted with bone marrow from the other mouse strain. Healing was assessed using ear hole diameter measurement, a 14 point histological scoring scale for the cartilage defect and an adapted version of the Osteoarthritis Research Society International scale for assessment of osteoarthritis in mouse knee joints. PRINCIPAL FINDINGS: Normal and chimeric MRL mice showed significantly better healing of articular cartilage and ear wounds along with less severe signs of osteoarthritis after cartilage injury than the control strain. Contrary to our hypothesis, however, bone marrow transplant from MRL mice did not confer improved healing on the C57Bl/6 chimeras, either in regards to ear wound healing or cartilage repair. CONCLUSION AND SIGNIFICANCE: The elusive cellular basis for the MRL regenerative phenotype still requires additional study and may possibly be dependent on additional cell types external to the bone marrow.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".