Markers of Systemic Inflammation in Horses with Heaves
Bibliographic record
Abstract
BACKGROUND: Systemic inflammation in horses with heaves is poorly characterized. OBJECTIVES: To assess acute phase proteins (APP) and inflammatory cytokine profiles in serum of healthy horses and horses with heaves. ANIMALS: Six healthy horses and 6 heaves-affected horses belonging to the University of Montreal. METHODS: Prospective, observational study. Healthy and heaves-affected control horses were exposed to a 30-day natural challenge with hay and straw to induce clinical exacerbation of heaves. Serum samples were obtained by venipuncture before (T0) as well as after 7 (T7) and 30 days (T30) of stabling. Serum APP (haptoglobin, serum amyloid A protein [SAA] and C-reactive protein [CRP]) and cytokines (IL-2, IL-4, IFN-α, IL-10, IFN-γ, and CCL-2) were measured using singleplex or multiplex ELISA. RESULTS: Serum haptoglobin concentrations were significantly higher in heaves-affected horses at all time points with no overlap with those of healthy controls. They were also significantly increased by antigen challenge in both controls (T7) and horses with heaves (T7 and T30). Serum SAA was detected more frequently in heaves-affected horses compared with healthy controls at T7. There was no difference in serum concentrations of CRP, IL-10, IFN-γ, and CCL-2 between groups, whereas IL-2, IL-4, and IFN-α remained undetectable in all samples. CONCLUSIONS AND CLINICAL IMPORTANCE: In heaves, haptoglobin is a marker of both acute and chronic systemic inflammation, whereas high concentrations of SAA indicate acute inflammation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".