Follicle-Stimulating Hormone Synthetic Peptides Inhibits Proliferation of Ovarian Cancer Cells by PI3K/Akt-cyclinD1 Pathway
Bibliographic record
Abstract
Objective:To observe the inhibit effect of follicle-stimulating hormone(FSH) synthetic peptides(FSH binding Fragment) on the human epithelial ovarian cancer cell.Methods:Human epithelial ovarian cancer cell line hey was incubated with FSH, FSH binding fragment,FSH and FSH binding fragment respectively.The cell proliferation was detected by methyl thiazolyl tetrazolium (MTT) technique 1.cyclinD1、Akt、pAkt were detected by western blot.Results:The proliferation of hey was increased apparently by FSH in a dose-dependent manner and the rate of proliferation average value 21%.Follicle-stimulating hormone(FSH) synthetic peptides inhibited the growth activity of hey,the rate of inhibition average value 22%.And the expression of cyclinD1 was also inhibited up 70%at the concentration of 2.5×10~(-9) Mol/L.Follicle-stimulating hormone(FSH) synthetic peptides inhibited the effect of FSH on hey,included the growth activity(18%)and the expression of cyclinD1(61%).FSH did not have effect on Akt,but pAkt was increased apparently,and pAkt/Akt was elvated 224%at the concentration of 40mIU/ml.Follicle-stimulating hormone(FSH) synthetic peptides decreased the pAkt in a time-dependent manner(almost 31%at 15 minutes) and in a dose-dependent manner(almost 66%at 2.5×10~(-9) Mol/L),but it also did not have effect on Akt.Follicle-stimulating hormone(FSH) synthetic peptides could inhibit the effect of FSH on pAkt(80%).Conclusions: It is suggested that FSH binding fragment can inhibit the proliferation of ovarian cancer cell stimulated by FSH though the PO3K/Akt-cyclinD1 pathway.
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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".