OVEREXPRESSION OF THE TYPE II TRANSFORMING GROWTH FACTOR‐β RECEPTOR INHIBITS FIBROBLAST PROLIFERATION AND ACTIVATES EXTRACELLULAR SIGNAL REGULATED KINASE AND C‐JUN N‐TERMINAL KINASE
Bibliographic record
Abstract
Transforming growth factor-beta (TGF-beta) is a bimodal regulator of cellular growth. The cellular effects of TGF-beta depend on the intensity of signals emanating from TGF-beta receptors. Low levels of receptor activity are sufficient to stimulate cell proliferation, while higher degrees of receptor activation are associated with growth inhibition. To study the mechanisms of these effects, a tetracycline-inducible expression system was used to overexpress type II TGF-beta receptors in NIH 3T3 fibroblasts. Overexpressed type II TGF-beta receptors suppressed fibroblast proliferation elicited by TGF-beta1, fibroblast growth factor (FGF) or platelet-derived growth factor (PDGF). Accompanying these anti-proliferative effects, increases in extracellular-signal regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) activity were detected. Furthermore, PDGF alpha-, but not PDGF beta-receptor protein levels were reduced by type II TGF-beta receptor overexpression. In conclusion, our system is an excellent tool to study the molecular mechanisms of growth inhibition by TGF-beta in fibroblasts. Activation of JNK and ERK, or modulation of PDGF receptor expression may be involved in this process.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".