TGF-β3 Signaling in Redifferentiating Passaged Human Articular Chondrocytes
Bibliographic record
Abstract
Bioengineering articular cartilage for the repair of damaged cartilage is one approach to biological joint repair. TGF-β3 has been shown to promote articular cartilage tissue formation by dedifferentiated passaged human chondrocytes. We hypothesize that TGF-β3 signals through both ALK1 and ALK5 in passaged human chondrocytes, to induce cartilage tissue formation in vitro. Treatment of passaged chondrocytes in 3D-culture with 10ng/ml TGF-β3 resulted in increased ALK5 expression and decreased ALK1 expression. This correlated with chondrocyte redifferentiation, as shown by increased gene expression of type II collagen and aggrecan, and tissue formation including increased collagen synthesis and retention of newly synthesized proteoglycans and collagen. This study identified that TGF-β3 promotes redifferentiation and ECM accumulation in passaged OA chondrocytes in part by signaling through both the canonical (ALK1 and ALK5) and the non-canonical (TAK1 and p38) pathways, though it is a complex regulation. Collagen production appears to be modulated through the ALK1/ALK5-TAK1-p38 axis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".