Seasonal Effect of Gonadotrophin Inhibitory Hormone on <scp>Gonadotrophin‐Releasing Hormone</scp>‐induced Gonadotroph Functions in the Goldfish Pituitary
Bibliographic record
Abstract
We have shown that native goldfish gonadotrophin inhibitory hormone (gGnIH) differentially regulates luteinsing hormone (LH)-β and follicle-stimulating hormone (FSH)-β expression. To further understand the functions of gGnIH, we examined its interactions with two native goldfish gonadotrophin-releasing hormones, salmon gonadotrophin-releasing hormone (sGnRH) and chicken (c)GnRH-II in vivo and in vitro. Intraperitoneal injections of gGnIH alone reduced serum LH levels in fish in early and mid gonadal recrudescence; this inhibition was also seen in fish co-injected with either sGnRH or cGnRH-II during early recrudescence. Injection of gGnIH alone elevated pituitary LH-β and FSH-β mRNA levels at early and mid recrudescence, and FSH-β mRNA at late recrudescence. Co-injection of gGnIH attenuated the stimulatory influences of sGnRH on LH-β in early recrudescence, and LH-β and FSH-β mRNA levels in mid and late recrudescence, as well as the cGnRH-II-elicited increase in LH-β, but not FSH-β, mRNA expression at mid and late recrudescence. sGnRH and cGnRH-II injection increased pituitary gGnIH-R mRNA expression in mid and late recrudescence but gGnIH reduced gGnIH-R mRNA levels in late recrudescence. gGnIH did not affect basal LH release from perifused pituitary cells and continual exposure to gGnIH did not alter the LH responses to acute applications of GnRH. However, a short 5-min GnIH treatment in the middle of a 60-min GnRH perifusion selectively reduced the cGnRH-II-induced release of LH. These novel results indicate that, in goldfish, gGnIH and GnRH modulate pituitary GnIH-R expression and gGnIH differentially affects sGnRH and cGnRH-II regulation of LH secretion and gonadotrophin subunit mRNA levels. Furthermore, these actions are manifested in a reproductive stage-dependent manner.
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".