Interaction of thyroid hormones and gonadotropin inhibitory hormone in the multifactorial control of zebrafish ( <i>Danio rerio</i> ) spermatogenesis
Bibliographic record
Abstract
ABSTRACT Reproduction is under multifactorial control of neurohormones, pituitary gonadotropins, as well as a number of gonadal hormones including sex steroids and growth factors. Gonadotropin-inhibitory hormone (Gnih), a novel RFamide neuropeptide, was shown to be involved in the control of pituitary gonadotropin production, as well as being involved as a paracrine factor in the regulation of gonadal function. In this context, recent studies have demonstrated that Gnih inhibited gonadotropin-induced spermatogenesis in the zebrafish testicular explants. Thyroid hormones are known to interact with the reproductive axis, and are, in particular, involved in the regulation of testicular function. Based on this background, we investigated the interaction between Gnih and thyroid hormones in the control of zebrafish spermatogenesis. To this end, zebrafish adult males were treated with the goitrogen methimazole (1mM for 21 days) in order to generate a hypothyroid model organism. Subsequently, a factorial design using an ex vivo testis culture system in combination with histomorphometrical and FACScan cell cycle analyses were adopted. Our results showed that methimazole treatment affected both basal and gonadotropin-induced spermatogenesis, in particular, meiosis and spermiogenesis. Moreover, the goitrogen treatment nullified the inhibitory actions of Gnih on the gonadotropin-induced spermatogenesis, specifically in the haploid cell population. We have demonstrated that thyroid hormones interaction with gonadotropin and Gnih are important components for the regulation of zebrafish spermatogenesis. The results provide a support for the hypothesis that thyroid hormones are important contributors in multifactorial control of spermatogenesis in zebrafish.
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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".