Transforming growth factor‐alpha induces endothelin receptor A expression in osteoarthritis
Bibliographic record
Abstract
Previously, our lab identified transforming growth factor-alpha (TGFα) as a novel factor involved in osteoarthritis (OA) in a surgical model of the disease. In the same study, we also observed increased transcript levels for endothelin receptor A (ET(A)R), a known contributor to cartilage pathology. To investigate the connection between TGFα and endothelin signaling in OA, primary articular chondrocytes and osteochondral explants were isolated from Sprague-Dawley rats and treated with vehicle or TGFα. Expression of ET(A)R protein and its encoding gene Ednra was assessed. Chondrocytes and cartilage explants were also treated with the endothelin receptor A/B antagonist Bosentan, in order to determine whether TGFα effects could be blocked. TGFα induced expression of ET(A)R protein and its encoding gene Ednra. In primary chondrocyte cultures, Bosentan did not block TGFα responses of the anabolic genes Sox9, Agc1, and Col2a1, but reduced the induction of Mmp13 and Ednra transcripts by TGFα. In osteochondral explants, the inhibitor partially blocked TGFα reduction of type II collagen, as well as induction of MMP-13 and type II collagen neoepitopes. TGFα induces ET(A)R expression in articular chondrocytes and receptor antagonism appears to block some TGFα-induced catabolic effects in a three-dimensional organ culture system. Thus, TGFα may be a therapeutic target upstream of ET(A)R in OA.
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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) | 0.001 | 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".