Expression of ski can act as a negative feedback mechanism on retinoic acid signaling
Bibliographic record
Abstract
BACKGROUND: Retinoic acid signaling is essential for many aspects of early development in vertebrates. To control the levels of signaling, several retinoic acid target genes have been identified that act to suppress retinoic acid signaling in a negative feedback loop. The nuclear protein Ski has been extensively studied for its ability to suppress transforming growth factor-beta (TGF-β) signaling but has also been implicated in the repression of retinoic acid signaling. RESULTS: We demonstrate that ski expression is up-regulated in response to retinoic acid in both early Xenopus embryos and in human cell lines. Blocking retinoic acid signaling using a retinoic acid antagonist results in a corresponding decrease in the levels of ski mRNA. Finally, overexpression of SKI in human cells results in reduced levels of CYP26A1 mRNA, a known target of retinoic acid signaling. CONCLUSIONS: Our results, coupled with the known ability of Ski to repress retinoic acid signaling, demonstrate that Ski expression is a novel negative feedback mechanism acting on retinoic acid signaling.
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 | 0.001 |
| 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".