HCG increases trophoblast migration in vitro via the insulin-like growth factor-II/mannose-6 phosphate receptor
Bibliographic record
Abstract
We have previously shown that both HCG and insulin-like growth factor-II (IGF-II) stimulate trophoblastic invasion. Furthermore, the invasion-promoting function of IGF-II resulted from IGF-II mannose 6-phosphate receptor (IGF-II/M6PR) activation. Since HCG and IGF-II did not have an additive effect on cell migration of extravillous trophoblast (EVT) cell line, HTR-8 SVneo, we hypothesized that HCG actions are mediated via alterations in the expression and/or function of IGF-II axis. HCG treatment (50-50,000 mU/ml) of the HTR-8/SVneo cells did not alter the expression of either insulin-like growth factor-I or IGF-II mRNA or peptide synthesis, but caused (i) an increase in the (125)I-IGF-II binding to EVT cells, and (ii) an increase in the externalization rate of the IGF-II binding sites without affecting their internalization. This effect was due to the increase in the number of IGF-II binding sites in the plasma membrane without any change in the IGF-II binding affinity. Although HCG did not influence the abundance of IGF-II/M6PR mRNA or protein, anti-IGF-II/M6PR antibody decreased HCG-induced migration of EVT, supporting the hypothesis that HCG might stimulate EVT migration by increasing IGF-II binding to the plasma membrane and subsequently by increasing the IGF-II effect probably mediated via the IGF-II/M6PR.
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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 | 0.000 |
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".