Changes in brain mRNA levels of gonadotropin-releasing hormone, pituitary adenylate cyclase activating polypeptide, and somatostatin during ovulatory luteinizing hormone and growth hormone surges in goldfish
Bibliographic record
Abstract
In goldfish, circulating LH and growth hormone (GH) levels surge at the time of ovulation. In the present study, changes in gene expression of salmon gonadotropin-releasing hormone (sGnRH), chicken GnRH-II (cGnRH-II), somatostatin (SS) and pituitary adenylate cyclase activating polypeptide (PACAP) were analyzed during temperature- and spawning substrate-induced ovulation in goldfish. The results demonstrated that increases in PACAP gene expression during ovulation are best correlated with the GH secretion profile. These results suggest that PACAP, instead of GnRH, is involved in the control of GH secretion during ovulation. Increases of two of the SS transcripts during ovulation are interpreted as the activation of a negative feedback mechanism triggered by high GH levels. The results showed a differential regulation of sGnRH and cGnRH-II gene expression during ovulation, suggesting that sGnRH controls LH secretion, whereas cGnRH-II correlates best with spawning behavior. This conclusion is further supported by the finding that nonovulated fish induced to perform spawning behavior by prostaglandin F2alpha treatment increased cGnRH-II expression in both forebrain and midbrain, but decreased sGnRH expression in the forebrain.
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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 | 0.000 | 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".