405 Effect of Subacute Ruminal Acidosis (SARA) and Saccharomyces cerevisiae fermentation products on inflammatory responses of dairy cows
Bibliographic record
Abstract
Abstract Feeding high-grain diets to dairy cows increases concentrations of acute phase proteins in peripheral blood. This inflammatory response can be reduced by supplementation with Saccharomyces cerevisiae fermentation products (SCFP). It is not clear which cytokines drive this inflammatory response, which organs are inflamed, and how these processes are affected by SCFP. The objectives of this study were to investigate if grain-induced SARA increases the concentrations of the cytokines IL-1β in peripheral blood, and of the inflammatory marker myeloperoxidase (MPO) in rumen papillae of lactating dairy cows, and if these concentrations are affected by SCFP. Thirty-two lactating Holstein dairy cows were randomly assigned to four treatment groups (n = 8) that received a TMR (34.9 %DM NDF, 18.6 %DM starch) supplemented with 1) 140 g/d of ground corn (Control), 2) 126 g/d corn and 14 g/d of Diamond V Original XPCTM (XPC), 3) 121 g/d corn and 19 g/d Diamond V NutriTek® (NTL), and 4) 102 g/d corn and 38 g/d of Diamond V NutriTek® (NTH). SARA challenges were conducted during wk 5 (SARA1) and 8 (SARA2) of lactation by replacing 20% of the base TMR with pellets containing 50% barley and 50% wheat. Blood samples were taken weekly between wk 4 (preSARA1) and wk 9 (postSARA2) for the analysis of IL-1β. Rumen papillae samples were taken during wk 3 (preSARA1) and wk 9 (postSARA2) for the measurement of MPO. SCFP treatment did not affect the concentrations of IL-1β, but the SARA challenges increased this concentration moderately from 9.0 to 12.3 pg/mL (P < 0.05). The concentration of MPO did not differ between preSARA1 and postSARA2, and these concentrations were not affected by SCFP. Results suggest that IL-1β may drive the acute phase response during the SARA challenges, and that these challenges did not cause inflammation of rumen papillae.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".