Bibliographic record
Abstract
The transforming growth factor-beta (TGF-beta) superfamily of secreted polypeptide growth factors exerts extensive control over all aspects of development and homeostasis, and components of this pathway are often mutated in cancers and in several hereditary disorders. Apart from TGF-beta, the superfamily also includes the activins and the bone morphogenetic proteins. These factors signal through heteromeric complexes of type II and type I serine-threonine kinase receptors, which activate the downstream Smad signal transduction pathway. Three classes of Smads have been defined: the receptor-regulated Smads (R-Smads), the common-mediator Smads (co-Smads), and the antagonistic or inhibitory Smads (I-Smads). Receptor complexes activate the Smad pathway by interacting and phosphorylating specific R-Smads. Phosphorylation of the R-Smads causes dissociation from the receptor and induces assembly into complexes with Smad4, a co-Smad. This heteromeric complex then translocates into the nucleus, where the Smads function as transcriptional comodulators by recruiting coactivators or corepressors to Smad DNA binding partners. Thus, Smads transmit signals directly from the receptor kinase into the nucleus. Crosstalk between Smads and other signaling pathways occurs both in the cytosol and in the nucleus. In the cytosol, Smad translocation might be inhibited by mitogen-activated protein kinase-dependent phosphorylation, whereas in the nucleus Smads interact with a number of transcription factors that themselves are primary targets of other signaling pathways. Furthermore, Smad-dependent regulation of these targets often requires input from the primary signaling pathway. In these examples, Smad signaling may represent a secondary signal that modifies the output of the primary pathway. Consequently, the transcriptional response to TGF-beta family ligands may be dependent on what other signals are being received by the cell. Crosstalk may thus provide one explanation for the long-standing observation that the biological response to TGF-beta is often dependent on the extracellular environment of the cell.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.023 |
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".