Expression of fibroblast growth factor 10 and its receptor, fibroblast growth factor receptor 2B, in the bovine corpus luteum
Bibliographic record
Abstract
There is evidence that several fibroblast growth factors (FGFs) are involved in growth and development of the corpus luteum (CL), but many FGFs have not been investigated in this tissue, including FGF10. The objective of this study was to determine if FGF10 and its receptor (FGFR2B) are expressed in the CL. Bovine CL were collected from an abattoir and classed as corpus hemorrhagica (stage I), developing (stage II), developed (stage III), and regressed (stage IV) CL. Expression of FGF10 and FGFR2B mRNA was measured by reverse transcription-polymerase chain reaction (RT-PCR). Both genes were expressed in bovine CL, and FGF10 expression did not differ between stages of CL development. FGF10 protein was localized to large and small luteal cells by immunohistochemistry. FGFR2B expression was approximately threefold higher in regressed compared to developing and developed CL (P < 0.05). To determine if FGF10 and FGFR2B expression is regulated during functional luteolysis, cattle were injected with PGF2alpha and CL collected at 0, 0.5, 2, 4, 12, 24, 48, and 64 hr thereafter (n = 5 CL/time point), and mRNA abundance was measured by real-time RT-PCR. FGF10 mRNA expression did not change during functional luteolysis, whereas FGFR2B mRNA abundance decreased significantly at 2, 4, and 12 hr after PGF2alpha, and returned to pretreatment levels for the period 24-64 hr post-PGF2alpha. These data suggest a potential role for FGFR2B signaling during structural luteolysis in bovine CL.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".