Decorin-Mediated Inhibition of Proliferation and Migration of the Human Trophoblast via Different Tyrosine Kinase Receptors
Bibliographic record
Abstract
Decorin (DCN), a decidua-derived TGFbeta-binding proteoglycan, negatively regulates proliferation, migration, and invasiveness of human extravillous trophoblast (EVT) cells in a TGFbeta-independent manner. The present study examined underlying mechanisms, in particular possible roles of epidermal growth factor receptor (EGFR), IGF receptor (IGFR)-I, and vascular endothelial growth factor receptor (VEGFR)-2. EVT cell sprouting from first-trimester chorionic villus explants in the presence or absence of TGFbeta-neutralizing antibody was inhibited with DCN, suggesting its negative regulatory role in situ. Inhibition of migration of the human EVT cell line HTR-8/SVneo in transwells undercoated with fibronectin was stronger when cells were briefly preincubated with DCN at 4 C (known to retard dissociation of receptor-ligand complex) than at 37 C, suggesting possible DCN action by cell membrane binding. Pretreatment of cells with an IGFR-I blocking agent, but not two EGFR blocking agents or a VEGFR blocking agent, significantly abrogated migration inhibitory effects of DCN, suggesting the involvement of IGFR-I but not EGFR or VEGFR in migration inhibition by DCN. On the other hand, pretreatment with either of the EGFR blocking agents, or the VEGFR blocking agent but not the IGFR-I blocking agent, blocked proliferation inhibitory effects of DCN, indicating the roles of EGFR and VEGFR, but not IGFR-I in antiproliferative action of DCN. EVT cells expressed EGFR, IGFR-I, and VEGFR-2. IGFR-I and VEGF-R2 were phosphorylated in the presence of their natural ligands as well as DCN, and these events were blocked by pretreatment with respective receptor blocking agents indicating DCN-mediated activation of these receptors. In conclusion, DCN effects on EVT cells are mediated selectively by multiple tyrosine kinase receptors.
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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".