The Combined Anti-inflammatory Strategy of Beta-2 Adrenergic Agonist and Glucocorticoid on the Laying Hen Model of Ovarian Cancer: the Immune Traits and Ovarian Inflammatory Functions
Bibliographic record
Abstract
Abstract Background: Ovarian cancer known as one of the most lethal gynecological malignancies mainly in older women, has been documented to link with chronic inflammation. Objective: This study was aimed to evaluate the combined strategy of glucocorticoid (GC) Fluticasone and beta-2 adrenergic agonist (BAA) Salmeterol on the ovarian inflammatory functions of the laying hen as a model of women ovarian cancer. Methods: White Leghorn hens aged 92 weeks were used for four weeks to be supplemented by individual of GC and BAA administration and their combination at three ratios GC:BAA 1:4 (GC+BAA1), 1:2 (GC+BAA2), and 1:1 (GC+BAA3), and beta blocker (BB) Propranolol. Ovulation rate and follicular growth were determined based on laying frequency and visual evaluation, respectively, the mRNA expressions of follicular beta-2 adrenergic receptor (β2ADR), cyclooxygenases (COX) 1 and 2, and cytokines were measured by real-time PCR. The plasma concentration of ovarian hormones cellular and humoral immune responses were measured via ELISA, neutrophil (heterophil) to lymphocyte ratio (NLR), and sheep red blood cell (SRBC) test, respectively. Results: As compared to control, combination groups of GC+BAA1 and 2 brought about a significant decrease in the mRNA expression of β2ADR, COX-2, and cytokines (P< 0.05). The ovulation rate was reduced in all combination ratios (P< 0.05). A significant reduction was observed in the plasma estradiol content on GC+BAA groups (P< 0.05) and the content of progesterone and androgen were statistically similar in some of these groups. Although NLR was similar in GC+BAA2 and 3, these groups had more content in the whole immunoglobulin (Ig) and IgM (P< 0.05). The results indicated that body weight and food consummation were decreased in all of the combination groups (P< 0.05). Conclusion: The GC and BAA combination may result in a significant reduction in some of the boosting factors on ovarian cancer, like ovulation intensity, pro-inflammatory mediators, and some of the ovarian steroid hormones that could define as the therapeutic approaches for the chronic inflammation-based carcinomas like ovarian cancer.
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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 | 0.001 |
| 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".