Seasonal effects of GnIH on basal and GnRH-induced goldfish somatotrope functions
Bibliographic record
Abstract
To understand how gonadotropin-inhibitory hormone (GnIH) regulates goldfish GH cell functions, we monitored GH release and expression during early, mid-, and/or late gonadal recrudescence. In vivo and in vitro responses to goldfish (g) GnIH were different, indicating direct action at the level of pituitary, as well as interactions with other neuroendocrine factors involved in GH regulation. Injection of gGnIH consistently reduced basal serum GH levels but elevated pituitary gh mRNA levels, indicating potential dissociation of GH release and synthesis. Goldfish GnRH (sGnRH and cGnRHII) injection differentially stimulated serum GH and pituitary gh mRNA levels with some seasonal differences; these responses were reduced by gGnIH. In contrast, in vitro application of gGnIH during 24-h static incubation of goldfish pituitary cells generally elevated basal GH release and attenuated sGnRH-induced changes in gh mRNA, while suppressing basal gh mRNA levels at mid- and late recrudescence but elevating them at early recrudescence. gGnIH attenuated the GH release responses to sGnRH during static incubation at early, but not at mid- and late recrudescence. In cell column perifusion experiments examining short-term GH release, gGnIH reduced the cGnRHII- and sGnRH-stimulated secretion at late recrudescence but inhibited tha action of cGnRHII only during mid-recrudescence. Interestingly, a reduction of basal GH release upon perifusion with gGnIH during late recrudescence was followed by a rebound increase in GH release upon gGnIH removal. These results indicate that gGnIH exerts complex effects on basal and GnRH-stimulated goldfish GH cell functions and can differentially affect GH release and mRNA expression in a seasonal reproductive manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".