Investigation of insulin-like growth factor-1 gene with egg-laying traits in the Muscovy duck (<i>Cairina moschata</i>)
Bibliographic record
Abstract
Muscovy duck (Cairina moschata) is characterized by broodiness, and egg-laying traits in this species should be of primary concern. As a member of the IGF superfamily of proteins, insulin-like growth factor-1 (IGF-1) is an important positive regulator of growth and gonad development in vertebrates. To explore the effect of IGF-1 on the growth hormone/insulin-like growth factors (GH/IGF) axis, we cloned and characterized IGF-1 of Muscovy duck. The expression level of IGF-1 was widely expressed in all the tested tissues, and the highest level was detected in the liver. In laying ducks, IGF-1 expression levels in the hypothalamus, pituitary, and ovary were very significantly higher (P < 0.01) than that in the tissues of nesting duck, and IGF-1 expression levels in the heart and liver were significantly higher (P < 0.05) than that in nesting duck tissues. Furthermore, a single nucleotide polymorphism (SNP) (A/G) was found and significantly (P < 0.05) associated with age at first egg and egg number at 300 d. This study provides the first evidence that IGF-1 promote egg-laying traits of Muscovy duck through two axes, involving GH/IGF and hypothalamic–pituitary–gonadal axes. These findings enrich the information of IGF-1 of Muscovy duck and demonstrate for the first time the ability of IGF-1 to promote reproduction, indicating that IGF-1 could be used as an important marker gene.
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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".