Matrix from urine stem cells boosts tissue-specific stem cell mediated functional cartilage reconstruction
Bibliographic record
Abstract
Articular cartilage has a limited capacity to self-heal once damaged. Tissue-specific stem cells are a solution for cartilage regeneration; however, ex vivo expansion resulting in cell senescence remains a challenge as a large quantity of high-quality tissue-specific stem cells are needed for cartilage regeneration. Our previous report demonstrated that decellularized extracellular matrix (dECM) deposited by human synovium-derived stem cells (SDSCs), adipose-derived stem cells (ADSCs), urine-derived stem cells (UDSCs), or dermal fibroblasts (DFs) provided an ex vivo solution to rejuvenate human SDSCs in proliferation and chondrogenic potential, particularly for dECM deposited by UDSCs. To make the cell-derived dECM (C-dECM) approach applicable clinically, in this study, we evaluated ex vivo rejuvenation of rabbit infrapatellar fat pad-derived stem cells (IPFSCs), an easily accessible alternative for SDSCs, by the abovementioned C-dECMs, in vivo application for functional cartilage repair in a rabbit osteochondral defect model, and potential cellular and molecular mechanisms underlying this rejuvenation. We found that C-dECM rejuvenation promoted rabbit IPFSCs' cartilage engineering and functional regeneration in both ex vivo and in vivo models, particularly for the dECM deposited by UDSCs, which was further confirmed by proteomics data. RNA-Seq analysis indicated that both mesenchymal-epithelial transition (MET) and inflammation-mediated macrophage activation and polarization are potentially involved in the C-dECM-mediated promotion of IPFSCs’ chondrogenic capacity, which needs further investigation.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".