SnoN as a novel negative regulator of TGF-β/Smad signaling: a target for tailoring organ fibrosis
Bibliographic record
Abstract
Remodeling of the extracellular matrix is beneficial during the acute wound healing stage following tissue injury. In the short term, resident fibroblasts and myofibroblasts regulate the matrix remodeling process through production of matricellular protein components that provide structural support to the damaged tissue. This process is largely governed by the transforming growth factor-β1 (TGF-β1) pathway, a critical mediator of the remodeling process. In the long term, chronic activation of the TGF-β1 pathway promotes excessive synthesis and deposition of matrix proteins, including fibrillar collagens, which ultimately leads to organ failure. SnoN (and its alternatively-spliced isoforms SnoN2, SnoA, and SnoI) is one of four members of a family of negative regulators of TGF-β1 signaling that includes Ski and functional Smad-suppressing elements on chromosomes 15 and 18. SnoN has been shown to be structurally and functionally similar to Ski and has been demonstrated to directly interact with Ski to abrogate gene expression. Despite this, little progress has been made in delineating a specific role for SnoN in the regulation of myofibroblast phenotype and function. This review outlines the current body of knowledge of what we refer to as the "Ski-Sno superfamily," with a focus on the structural and functional importance of SnoN in mediating the fibrotic response by myofibroblasts following tissue injury.
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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 | 0.001 |
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".