Activated receptor tyrosine kinases in granulosa cells of ovulating follicles in mice
Bibliographic record
Abstract
Successful ovulation requires the actions of gonadotropins along with those mediated by growth factors binding to their receptor tyrosine kinases (RTKs). There are several growth factors such as epidermal growth factor family ligands and interleukins that play a role during ovulation initiated by the preovulatory surge of luteinizing hormone (LH). The aim of this project was to analyze growth factor signaling pathways induced by LH in mouse granulosa cells. Immature female mice were treated with equine chorionic gonadotropin (eCG) followed 48 hr later by human chorionic gonadotropin (hCG) to induce follicular growth and ovulation. We performed protein array analysis where we identified higher phosphorylation of insulin-like growth factor 1 receptor (IGF1R), the fibroblast growth factor receptor 2 (FGFR2) and ephrin receptor B1 (EPHB1) in granulosa cells at 4 hr post-hCG compared to 0 hr hCG (p < 0.05). We report both a significant increase in transcript abundance (p < 0.05) and the phosphorylation level (p < 0.05) of the IGF1R in granulosa cells at hCG4h. The mRNA abundance of the Fgfr2 and Ephb1 receptors remained unaltered upon hCG treatment. Nonetheless, transcript abundance of the fibroblast growth factor 2 (Fgf2) ligand was elevated at hCG4h (p < 0.01). Based on these results we conclude that the preovulatory LH surge activates signaling pathways of IGF1R through increase in the expression of the Igf1r gene in granulosa cells of ovulating follicles in mice. The LH surge also appears to activate FGFR2 IIIc and EPHB1 signaling, although further investigation is required.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".