Effects of the Rate and Duration of Physiological Increases in Serum FSH Concentrations on Emergence of Follicular Waves in Cyclic Ewes1
Bibliographic record
Abstract
Large antral follicles grow in waves in the ewe, with each wave triggered by a peak in serum FSH concentrations. In this study, our objectives were to determine if the slope of the rise in the FSH peak affects the ability of the peak to trigger wave emergence (experiment 1), and whether increasing serum FSH concentrations and holding them at peak concentrations would provide a stimulus for constant emergence of large antral follicles (experiment 2). In experiment 1, cyclic ewes received ovine FSH (n = 6; 0.1 μg/kg, s.c.) or vehicle (n = 6; control) every 6 h for 42 h. This treatment created a peak in serum FSH concentrations (P < 0.05) during the early growth phase of the first follicular wave of the interovulatory interval and enhanced the growth of follicles in that wave (P < 0.05), but did not trigger emergence of a follicular wave. In experiment 2, cyclic ewes were infused constantly with oFSH (1.98 μg/h; n = 6) or vehicle (control; n = 6) for 60 h starting at the time of the second endogenously driven FSH peak of the interovulatory interval. Infusion of oFSH resulted in a super-stimulatory effect, with a peak in the mean number of large follicles (≥5 mm) on Day 2 after the start of FSH infusion (13 ± 1.2 large follicles per ewe, 1.8 ± 0.2 in control ewes; P < 0.001). In conclusion, exposing early growing antral follicles in a wave to a gradual increase in serum concentrations of FSH enhanced their growth, but did not trigger the expected new follicular wave, and infusion of a dose of oFSH within the physiological range caused a super-ovulatory response in cyclic ewes.
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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 | 0.001 |
| 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 | 0.001 |
| 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".