Regulation of heparin-binding EGF-like growth factor expression in Ha-ras transformed human Mammary epithelial cells
Bibliographic record
Abstract
Heparin-binding epidermal growth factor-like growth factor (HB-EGF) mRNA and protein expression is induced by EGF in MCF-10A nontransformed and Ha-ras transfected human mammary epithelial cells. The anti-EGF receptor (EGFR) blocking monoclonal antibody (MAb) 225 and the EGFR tyrosine kinase inhibitor PD153035 were able to inhibit the induction of HB-EGF mRNA levels in MCF-10A cells. However, the Ha-ras transformed MCF-10A cells were more refractory to inhibition by these agents and only a combination of the 225 MAb and PD153035 was able to significantly abrogate HB-EGF induction by EGF. The anti-erbB2 MAb L26 which interferes with heterodimer formation was able to block HB-EGF induction in response to EGF in MCF-10A cells and in the Ha-ras transformed cells only when used in combination with either the 225 MAb or PD153035. The MEK inhibitor PD90859 completely blocked EGF induction of HB-EGF mRNA levels in the nontransformed and Ha-ras transformed MCF-10A cells, which indicates that MAPK is involved in the signaling pathway of HB-EGF induction by EGF. An increase in the levels of HB-EGF may, therefore, be an important contributor to oncogenic transformation that is caused by Ha-ras overexpression in mammary epithelial cells. J. Cell. Physiol. 186:233-242, 2001. Published 2001 Wiley-Liss, Inc.
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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 | 0.000 | 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 | 0.001 |
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".