Profibrogenic phenotype in caveolin-1 deficiency via differential regulation of STAT-1/3 proteins
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
Fibrosis underlies the pathogenesis of several human diseases, which can lead to severe injury of vital organs. We previously demonstrated that caveolin-1 expression is reduced in experimental fibrosis and that caveolin-1 exerts antiproliferative and antifibrotic effects in lung fibrosis models. The signal transducers and activators of transcription (STAT) proteins, STAT1 and STAT3, can be activated simultaneously. STAT1 can inhibit cell growth and promote apoptosis while STAT3 inhibits apoptosis. Here, we show that caveolin-1-deficient (cav-1(-/-)) lung fibroblasts display dramatically upregulated STAT3 activation in response to platelet-derived growth factor-BB and transforming growth factor-β stimuli, whereas STAT1 activation is undetectable. Downregulation of protein tyrosine phosphatase-1B played a role in the preferential activation of STAT3 in cav-1(-/-) fibroblasts. Genetic deletion of STAT3 by siRNA modulated the expression of genes involved in cell proliferation and fibrogenesis. Basal expression of α-smooth muscle actin was prominent in cav-1(-/-) liver and kidney, consistent with deposition of collagen in these organs. Collectively, we demonstrate that the antiproliferative and antifibrogenic properties of caveolin-1 in vitro are mediated by the balance between STAT1 and STAT3 activation. Deregulated STAT signaling associated with caveolin-1 deficiency may be relevant to proliferative disorders such as tissue fibrosis.
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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".