Serum Surfactant Protein D and Haptoglobin as Potential Biomarkers for Inflammatory Airway Disease in Horses
Bibliographic record
Abstract
BACKGROUND: The identification of serum biomarkers of lung inflammation would facilitate the diagnosis of inflammatory airway disease (IAD) in horses. HYPOTHESIS: Horses with IAD have higher serum concentrations of markers of inflammation compared to controls. ANIMALS: Twelve horses with IAD and 10 control horses. METHODS: This was a prospective case-control study. Blood and BALF were collected from horses with IAD and controls. Serum concentration of surfactant protein D (SP-D), haptoglobin, serum amyloid A (SAA) and of the soluble form of triggering receptor expressed on myeloid cells 1 (sTREM-1) was measured using commercial ELISA tests. RESULTS: Horses with IAD had higher serum concentration (log-transformed values) of SP-D (mean ± SD: 1.773 ± 0.51), haptoglobin (6.657 ± 0.202) and SAA (0.128 ± 0.396) compared to controls (0.942 ± 0.226, 6.38 ± 0.22, -0.398 ± 0.319, respectively; P < .01 for all). Furthermore, the concentrations of SP-D and haptoglobin combined allowed differentiating the 2 groups (IAD: 8.43 ± 0.564, controls: 7.322 ± 0.249, P < .0001) with a sensitivity and specificity of 100% when a cut-off of 7.70 (log value) was employed. CONCLUSIONS AND CLINICAL IMPORTANCE: Surfactant protein D and haptoglobin serum concentrations could be a diagnostic aid in IAD. Further studies are necessary to establish the specificity of our findings before they can be applied in everyday practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".