Effects of Daidzein on Messenger Ribonucleic Acid Expression of Gonadotropin Receptors in Chicken Ovarian Follicles
Bibliographic record
Abstract
Effects of daidzein on expression of mRNA of gonadotropin receptors [follicle-stimulating hormone receptor (FSHR), luteinizing hormone receptor (LHR)] were evaluated in ovarian follicles of ISA laying hens that were 13 mo old in the postpeak period of egg laying. The hens were randomly allocated as control and daidzein-treated groups, with daidzein supplemented to the basal diet at the level of 10 mg/kg for 7 wk. The granulosa layers of preovulatory follicles (F1, F2, F3, F4, F5) and follicular layers of the small yellow follicle (SYF), large white follicle (LWF), and atretic follicle were collected. The mRNA expression of related genes was measured by semiquantitative reverse transcription PCR. Results showed that daidzein significantly increased the egg-laying rate (P < 0.05) and the number of SYF and LWF (P < 0.05). The relative abundance of the FSHR mRNA decreased in the granulosa layers from F5 to F1, but LHR mRNA displayed the opposite trend in developmental changes. Treatment with daidzein resulted in increased expression of FSHR mRNA in LWF, SYF, and granulosa layers of F4 to F2 and LHR mRNA in granulosa layers of F4 and F1 (P < 0.05). These results indicated that dietary supplementation of daidzein upregulated mRNA expression of gonadotropin receptors to improve follicle development in chicken developing follicles and laying performance after the peak laying period.
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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".