In Vivo Identification and Induction of Articular Cartilage Stem Cells by Inhibiting NF-κB Signaling in Osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a highly prevalent and debilitating joint disorder characterized by the degeneration of articular cartilage. However, no effective medical therapy has been found yet for such condition. In this study, we directly confirmed the existence of articular cartilage stem cells (ACSCs) in vivo and in situ for the first time both in normal and OA articular cartilage, and explored their chondrogenesis in Interleukin-1β (IL-1β) induced inflammation environment and disclose whether the inhibition of NF-κB signaling can induce ACSCs activation thus improve the progression of experimental OA. We found an interesting phenomenon that ACSCs were activated and exhibited a transient proliferative response in early OA as an initial attempt for self-repair. During the in vitro mechanism study, we discovered IL-1β can efficiently activate the NF-κB pathway and potently impair the responsiveness of ACSCs, whereas the NF-κB pathway inhibitor rescued the ACSCs chondrogenesis. The final in vivo experiments further confirmed ACSCs' activation were maintained by NF-κB pathway inhibitor, which induced cartilage regeneration, and protected articular cartilage from injury in an OA animal model. Our results provided in vivo evidence of the presence of ACSCs, and disclosed their action in the early OA stage and gradual quiet as OA process, presented a potential mechanism for both cartilage intrinsic repair and its final degradation, and demonstrated the feasibility of inducing endogenous adult tissue-specific mesenchymal stem cells for articular cartilage repair and OA therapy.
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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 | 0.001 |
| 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".