Microarray molecular mapping of horses with severe asthma
Bibliographic record
Abstract
BACKGROUND: Severe asthma (SA) in horses, resembling human asthma, is a prevalent, debilitating allergic respiratory condition marked by elevated allergen-specific immunoglobulin E (IgE) against environmental proteins; however, research exploring the exposome's influence on IgE profiles is currently limited but holds paramount significance for diagnostic and therapeutic developments. ANIMALS: Thirty-five sports horses were analyzed, consisting of environmentally matched samples from France (5 SA; 6 control), the United States (6 SA; 6 control), and Canada (6 SEA; 6 control). METHODS: This intentional cross-sectional study investigated the sensitization profiles of SA-affected and healthy horses via serological antigen microarray profiling. Partial least square-discriminant analysis (PLS-DA) was used to identify and rank the importance of allergens for class separation (ie, affected/non-affected) as variable influence of projection (VIP), and allergen with commonality internationally established via frequency analysis. RESULTS: PLS-DA models showed high discriminatory power in predicting SA in horses from Canada (area under the curve [AUC] 0.995) and France (AUC 0.867) but poor discriminatory power in horses from the United States (AUC 0.38). Hev b 5.0101, Cyn D, Der p 2, and Rum cr were the only shared allergens across all geographical groups. CONCLUSIONS AND CLINICAL IMPORTANCE: Microarray profiling can identify specific allergenic components associated with SA in horses, while mathematical modeling of this data can be used for disease classification, highlighting the variability of sensitization profiles between geographical locations and emphasizing the importance of local exposure to the prevalence of different allergens. Frequency scoring analysis can identify important variables that contribute to the classification of SA across different geographical regions.
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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 | 0.001 | 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".