Mesenchymal progenitor cells from non-inflamed versus inflamed synovium post-ACL injury present with distinct phenotypes and cartilage regeneration capacity
Bibliographic record
Abstract
Abstract Background Osteoarthritis (OA) is a chronic debilitating disease impacting a significant percentage of the global population. While there are numerous surgical and non-invasive interventions that can postpone joint replacement, there are no current treatments which can reverse the joint damage occurring during the pathogenesis of the disease. While many groups are investigating the use of stem cell therapies in the treatment of OA, we still don’t have a clear understanding of the role of these cells in the body, including heterogeneity of tissue resident adult mesenchymal progenitor cells (MPCs). Methods In the current study, we examined MPCs from the synovium and individuals with or without a traumatic knee joint injury and explored the chondrogenic differentiation capacity of these MPCs in vitro and in vivo. Results We found that there is heterogeneity of MPCs with the adult synovium and distinct sub-populations of MPCs and the abundancy of these sub-populations change with joint injury. Furthermore, only some of these sub-populations have the ability to effect cartilage repair in vivo. Using an unbiased proteomics approach, we were able to identify cell surface markers that identify this pro-chondrogenic MPC population in normal and injured joints, specifically CD82LowCD59+ synovial MPCs have robust cartilage regenerative properties in vivo. Conclusions The results of this study clearly show that cells within the adult human joint can impact cartilage repair and that these sub-populations exist within joints that have undergone a traumatic joint injury. Therefore, these populations can be exploited for the treatment of cartilage injuries and OA in future clinical trials.
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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 | 0.000 |
| 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".