Epidermal Growth Factor Enhancement of Insulin-Like Growth Factor-I-Induced Down-Regulation of Insulin-Like Growth Factor I Receptor in Cultured Placental Trophoblastic Cells
Bibliographic record
Abstract
Epidermal growth factor (EGF) and insulin-like growth factor I (IGF-I) synergistically stimulate placental lactogen (hPL) secretion by placental cells. To understand the mechanism of actions we have investigated a possible heterologous regulatory effect of EGF and IGF-I on each other's receptors. Pretreatment of the cells with IGF-I had no effect on [125I]-EGF binding or the down-regulation of EGF receptor. Pretreatment of the cells with EGF, concomitantly with IGF-I, had no effect on [125I]-IGF-I binding but it augmented the IGF-I down-regulation of IGF-I receptor. The time required to initiate the IGF-I-induced down-regulation of IGF-I receptor was reduced by 4 h in the presence of EGF. IGF-I-down-regulated decreased (P less than 0.05) receptor numbers were further decreased (p less than 0.05) in the presence of EGF. These results suggested that the synergistic effect of EGF and IGF-I seen in hPL secretion by placental cells is not due to direct heterologous hormone-receptor interactive effects. However, the effects seen may be due to a differentiating effect of EGF sensitizing the cells for responsiveness to IGF-I.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".