N-Terminal Pro-C-Natriuretic Peptide and Cytokine Kinetics in Dogs with Endotoxemia
Bibliographic record
Abstract
BACKGROUND: Serum N-terminal pro-C-natriuretic peptide (NT-proCNP) concentration at hospital admission has sufficient sensitivity and specificity to differentiate naturally occurring sepsis from nonseptic systemic inflammatory response syndrome (SIRS). However, little is known about serum NT-proCNP concentrations in dogs during the course of sepsis. OBJECTIVE: To determine serum NT-proCNP and cytokine kinetics in dogs with endotoxemia, a model of canine sepsis. SAMPLES: Eighty canine serum samples. METHODS: Eight healthy adult Beagles were randomized to receive Escherichia coli lipopolysaccharide (LPS, 5 μg/kg) or placebo (0.9% NaCl) as a single IV dose in a randomized crossover study. Serum collected at 0, 1, 2, 4, and 24 hours was stored at -80°C for batch analysis. Serum NT-proCNP was measured by ELISA and 13 cytokines and chemokines by multiplex magnetic bead-based assay. RESULTS: Serum NT-proCNP concentrations did not differ significantly between LPS- and placebo-treated dogs at any time. When comparing serum cytokine concentrations, LPS-treated dogs had higher interleukin-6 (IL-6), IL-10, TNF-α and KC-like at 1, 2, and 4 hours; higher CCL2 at 1, 2, 4, and 24 hours; and higher IL-8 and CXCL10 at 4 hours compared to placebo-treated dogs. There were no differences in serum GM-CSF, IFN-γ, IL-2, IL-7, IL-15 or IL-18 between LPS- and placebo-treated dogs. CONCLUSIONS AND CLINICAL IMPORTANCE: Serum NT-proCNP concentration does not change significantly in response to LPS administration in healthy dogs. Certain serum cytokine and chemokine concentrations are significantly increased within 1-4 hours after LPS administration and warrant further investigation as tools for the detection and management of sepsis in dogs.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".