Candidate serum metabolite biomarkers of subclinical Haemonchus contortus infection in sheep
Bibliographic record
Abstract
Parasitic infection is a major factor negatively affecting sheep production worldwide. The gold standard, fecal egg count, identifies only clinical stages after egg count establishment and shedding in the feces. However, detection at the sub-clinical phase could offer a critical window for strategic deworming and prevent production losses. Blood metabolites often perform as signalling molecules preceding major physiological events; thus, they could provide a prognosis of immunological alterations in the host. Therefore, we conducted a controlled longitudinal study to identify predictive biomarkers of Haemonchus contortus infection in 60 Rideau Arcott ewe lambs, analyzing 420 serum samples across seven timepoints (from one day pre-inoculation to 57-days post-inoculation [dpi]) using direct injection mass spectrometry and reverse-phase liquid chromatography-tandem mass spectrometry custom assay. We identified a total of 22 unique metabolites as candidate biomarkers of parasitic infection pre- and post-drenching, ranging in area-under-the-receiver-operating-characteristic-curve (AU-ROC) values 0.69–0.94 (p < 0.05). The earliest candidate biomarkers were detectable at 7-dpi, the most promising pre-drenching biomarkers emerged at 21-dpi, while the highest AU-ROC value post-drenching was at 42-dpi. This is the first study to identify predictive blood biomarkers of H. contortus infection in Rideau Arcott ewe lambs, including the relevant mathematical models. Further validation in larger cohorts could lead to a simple blood test for sub-clinical parasite infection detection, reducing anthelmintic use on sheep farms.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".