Pro-and anti-inflammatory effects of glucocorticoid Fluticasone on ovarian and immune functions in commercial-aged laying hens
Bibliographic record
Abstract
Ovarian chronic inflammation has been created and extended in the laying hen mainly via increasing laying frequency and microbial infection, especially during the late stage of production period. This study was aimed to evaluate glucocorticoid (GC) Fluticasone as an anti-inflammatory agent on the gene expression of the ovarian pro-and anti-inflammatory mediators (follicular cyclooxygenases COX 1, 2, and cytokines), inflammatory responses of the immune system, ovarian functions (ovulation rate and follicular growths), and hormones in the commercial-aged laying hens. White Leghorn hens aged 92-weeks were used for four weeks to be supplemented by 2 ppm Fluticasone as an optimum dose obtained in a pre-trial base on ovulation rate. As compared to control, Fluticasone resulted in a significant decrease in the mRNA expression of COX-1 and pro-and anti-inflammatory cytokines, and increase in COX-2 mRNA expression and heterophil to lymphocyte ratio (P < 0.001). A significant reduction was observed in the ovulation rate, follicular size (P < 0.001), ovarian hormones, immunoglobulins, body weight, and food consummation (P ≤ 0.05) by administering GC Fluticasone. Although a relative anti-inflammatory improvement was created by Fluticasone in the ovarian condition, the administration of this glucocorticoid resulted in a considerable reduction in ovarian hormones and functions of commercial aged laying hens.
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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".