Effect of Extra-expression of Estrogen Receptor in Restoration of Regulation of Estrogen on Notch Signaling Pathway in Endometrial Cancer Cells
Bibliographic record
Abstract
Objective:To observe the effect of estrogen on Notch signaling pathway in endometrial cancer cells,and to explore whether estrogen can induce the expression of Notch when estrogen receptor(ER) is up-regulated,further affecting the cell proliferation. Methods:MTT was used to measure the proliferative activity after various treatments.RT-PCR and Western-blotting were used to determine the effect of estrogen and DAPT on Notch expression in mRNA and protein levels.Plasmid was transfected into KLE to induce the expression of estrogen receptor.Results:Estrogen enhances endometrial cancer cells' proliferative activity,especially with the concentration of 1.0×10~(-9)M(1.25±0.026,P0.05).Inhibition of Notch signal pathway blocks estrogen induced cellular growth(0.76±0.02,P0.05).Expression of Notch is not changed by estrogen but up-regulated after transfection.Further more,the relative proliferative activity is enhanced(1.24±0.02,P0.05).Conclusions:In ER-negative endometrial cancer cells KLE,estrogen can activate Notch signal pathway when ER is up-regulated,and then enhance cells' proliferative activity.
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) | 0.000 | 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".