Comparison of Cytokine mRNA Expression in the Bronchoalveolar Lavage Fluid of Horses with Inflammatory Airway Disease and Bronchoalveolar Lavage Mastocytosis or Neutrophilia Using REST Software Analysis
Bibliographic record
Abstract
OBJECTIVES: The pathophysiology of inflammatory airway disease (IAD) is unknown, but in some cases involves the accumulation of mast cells, neutrophils, or both in the bronchoalveolar lavage fluid (BALF). The objective of this study was to characterize cytokine gene expression in the BALF cells of horses with IAD, including a comparison of cytokine gene expression between IAD horses with increased BALF mast cells (IAD-Mast) or neutrophils (IAD-Neutro). METHODS: The mRNA expression of IL-4, IFN-γ, IL-17, IL-8, IL-1β, IL-5, IL-6, IL-10, IL-12p35, and eotaxin-2 was studied by quantitative polymerase chain reaction (QPCR) with efficiency correction in BALF samples of 17 horses with IAD (IAD-total), also subcategorized as 8 IAD-Mast and 9 IAD-Neutro, and 10 controls. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as a reference gene. Relative expression software tool (REST) analysis provided ratios of expression, statistical analysis, and confidence intervals for the results. RESULTS: Compared with the control group, IL-5, IL-1β, IL-6, IL-8, and IL-10 mRNA expression was upregulated 3.5-, 3.4-, 2.8-, 2.2-, and 1.9-fold, respectively, in the IAD-total group. The IAD-Neutro group showed increased expression of IL-17, IL-8, and IL-5 (4.7-, 2.5-, and 2.9-fold, respectively) and a decreased expression of IL-4 (3.4-fold) compared with the IAD-Mast group. CONCLUSION: Cytokines from the Th2 family plays a key role in IAD and a different pathophysiology may be involved in mast cell versus neutrophil BALF accumulation in IAD horses.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".