Faculty Opinions recommendation of Synergistic interaction of two classes of transforming growth factors from murine sarcoma cells.
Bibliographic record
Abstract
Transforming growth factors (TGFs) isolated from murine sarcoma virus-transformed 3T3 cells have been separated by high-pressure liquid chromatography into two subsets. One subset, called TGF alpha, competes with epidermal growth factor (EGF) for receptor sites, whereas the other, called TGF beta, does not. TGB beta, purified by high-pressure liquid chromatography, will not induce formation of large colonies of cells in soft agar in the absence of TGF alpha or EGF. However, the combined action of either TGF alpha or EGF (which by themselves are relatively ineffective in promoting growth of cells in soft agar) together with TGF beta results in a potent synergistic effect, with formation of large colonies. Chemically modified analogs of EGF also potentiate TGF beta activity to the extent that they bind to the EGF receptor. It is suggested that TGF beta may be an important mediator of the known effects of both TGF alpha and EGF on neoplastic transformation. PMID: 6290046
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.025 |
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".