Bibliographic record
Abstract
The balance between growth stimulatory and growth inhibitory signals is essential for normal tissue homeostasis. An imbalance of these signals may favour the development of uncontrolled proliferation, leading to neoplasia. Transforming growth factor (TGF)-β plays an important role in the regulation of mammary duct development and cellular proliferation in the mammary gland during adult life. The different in vitro and in vivo models that have shed light on mammary development and the limitations of these models are presented in the first review of this series. The second article reviews murine models that have provided insights, not only into the role of TGF-β in mammary development, but also into its role in tumour suppression. TGF-β receptors and the Smad signalling molecules transduce TGF-β signals. The study of these has yielded important insights into the regulation of this pathway. A review of the many levels at which TGF-β signalling can be disrupted during oncogenesis is presented in this series. TGF-β is a potent mediator of G1 arrest in mammary epithelial and other cell types. Mechanisms of G1 arrest by TGF-β and their dysregulation in cancers are addressed. In a final review, the tumor promoting effects of TGF-β are discussed. In advanced stages of breast cancer, not only is the cell cycle arrest response to TGF-β lost, but, in addition, this pathway can be subverted in such a way that TGF-β signalling indirectly supports tumour viability, invasiveness and malignant progression.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.007 | 0.003 |
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".