The Hippo signal transduction network in skeletal and cardiac muscle
Bibliographic record
Abstract
The discovery of the Hippo pathway can be traced back to two areas of research. Genetic screens in fruit flies led to the identification of the Hippo pathway kinases and scaffolding proteins that function together to suppress cell proliferation and tumor growth. Independent research, often in the context of muscle biology, described Tead (TEA domain) transcription factors, which bind CATTCC DNA motifs to regulate gene expression. These two research areas were joined by the finding that the Hippo pathway regulates the activity of Tead transcription factors mainly through phosphorylation of the transcriptional coactivators Yap and Taz, which bind to and activate Teads. Additionally, many other signal transduction proteins crosstalk to members of the Hippo pathway forming a Hippo signal transduction network. We discuss evidence that the Hippo signal transduction network plays important roles in myogenesis, regeneration, muscular dystrophy, and rhabdomyosarcoma in skeletal muscle, as well as in myogenesis, organ size control, and regeneration of the heart. Understanding the role of Hippo kinases in skeletal and heart muscle physiology could have important implications for translational research.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".