Short communication: Characterizing ovine serum stress biomarkers during endotoxemia
Bibliographic record
Abstract
Breeding stress-resilient livestock is a potential strategy to help mitigate the negative effect of environmental and pathogenic stressors. The hypothalamic-pituitary-adrenal axis and immune system are activated during stress events and release mediators into the circulation that help restore physiological homeostasis. The purpose of this study was to assess a comprehensive set of circulatory mediators released in response to an acute immune stress challenge to identify candidate biomarkers that can be used for the selection of stress-resilient animals. Fifteen female lambs were stress challenged with an intravenous bolus of lipopolysaccharide (LPS; 400 ng/kg), and blood was collected from the jugular vein at 0, 2, 4, and 6 h after LPS challenge to identify and monitor candidate stress biomarkers; temperature was also recorded over time. Biomarker responses were evaluated with a repeated-measures model to compare time points with baseline values. As expected, all sheep had a monophasic febrile response to LPS challenge, and cortisol increased and returned to baseline by 6 h. The cytokines tumor necrosis factor-α, IL-6, IFN-γ (proinflammatory), and IL-10 (anti-inflammatory) increased, but only tumor necrosis factor-α returned to baseline during the monitoring period. The cytokines IL-1α, IL-1β, IL-17α (proinflammatory), and IL-4 (anti-inflammatory) did not respond to LPS challenge. All chemokines (CCL2, CCL3, CCL4, CXCL10, and IL-8) responded to LPS challenge; however, only CCL2, CCL3, CCL4, and CXCL10 increased over time, and only CCL3, CCL4, and CXCL10 returned to baseline during the monitoring period. MicroRNA (miR-145, miR-233, and miR-1246) also increased and remained elevated during the study. In summary, the LPS challenge induced a strong stress response in Rideau-Dorset sheep that could be monitored with a distinct profile of circulatory biomarkers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".