Recent advances in understanding contextual TGFβ signaling
Bibliographic record
Abstract
The appearance of the first animal species on earth coincides with the emergence of transforming growth factor β (TGFβ) pathways. The evolution of these animals into more complex organisms coincides with a progressively increased TGFβ repertoire through gene duplications and divergence, making secreted TGFβ molecules the largest family of morphogenetic proteins in humans. It is therefore not surprising that TGFβ pathways govern numerous aspects of human biology from early embryonic development to regeneration, hematopoiesis, neurogenesis, and immunity. Such heavy reliance on these pathways is reflected in the susceptibility to minor perturbations in pathway components that can lead to dysregulated signaling and a diverse range of human pathologies such as cancer, fibrosis, and developmental disorders. Attempts to comprehensively resolve these signaling cascades are complicated by the long-recognized paradoxical role the pathway plays in cell biology. Recently, several groups have probed examples of the disparate aspects of TGFβ biology in a variety of animal models and uncovered novel context-dependent regulatory mechanisms. Here, we briefly review recent advancements and discuss their overall impact in directing future TGFβ 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".