GSK-3β in mouse fibroblasts controls wound healing and fibrosis through an endothelin-1–dependent mechanism
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Glycogen synthase kinase-3 (GSK-3) is a widely expressed and highly conserved serine/threonine protein kinase encoded by 2 genes, GSK3A and GSK3B. GSK-3 is thought to be involved in tissue repair and fibrogenesis, but its role in these processes is currently unknown. To investigate the function of GSK-3beta in fibroblasts, we generated mice harboring a fibroblast-specific deletion of Gsk3b and evaluated their wound-healing and fibrogenic responses. We have shown that Gsk3b-conditional-KO mice (Gsk3b-CKO mice) exhibited accelerated wound closure, increased fibrogenesis, and excessive scarring compared with control mice. In addition, Gsk3b-CKO mice showed elevated collagen production, decreased cell apoptosis, elevated levels of profibrotic alpha-SMA, and increased myofibroblast formation during wound healing. In cultured Gsk3b-CKO fibroblasts, adhesion, spreading, migration, and contraction were enhanced. Both Gsk3b-CKO mice and fibroblasts showed elevated expression and production of endothelin-1 (ET-1) compared with control mice and cells. Antagonizing ET-1 reversed the phenotype of Gsk3b-CKO fibroblasts and mice. Thus, GSK-3beta appears to control the progression of wound healing and fibrosis by modulating ET-1 levels. These results suggest that targeting the GSK-3beta pathway or ET-1 may be of benefit in controlling tissue repair and fibrogenic responses in vivo.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".