Ruminal Lipopolysaccharide Concentration and Inflammatory Response During Grain-Induced Subacute Ruminal Acidosis in Dairy Cows
Bibliographic record
Abstract
The effects of grain-induced subacute ruminal acidosis (SARA) in lactating dairy cows on free ruminal lipopolysaccharide (LPS) and indicators of inflammation were determined. Four mid lactation dairy cows were divided into 2 groups of 2 cows and used in a repeated switchover design. During each period, SARA was induced in 2 animals for 5 subsequent days by replacing 25% of their total mixed ration (dry matter basis) with a concentrate made of 50% wheat and 50% barley. The other 2 cows acted as controls and were fed a total mixed ration diet in which 44% of dry matter was concentrate. On average, inducing SARA did not affect milk composition, increased the duration of rumen pH below 5.6 from 187 to 309 min/d, and increased free ruminal LPS concentration from 24,547 endotoxin units (EU)/mL to 128,825 EU/mL. Averaged across treatments, milk fat yield and milk protein yield were 0.66 and 1.00 kg/d, respectively. Rumen pH and milk fat data suggest that control cows also experienced ruminal acidosis, albeit a milder form of this disease than SARA cows. Serum LPS concentration in both control and SARA cows was less than the detection limit of <0.01 EU/mL for the assay. Induction of SARA elevated serum amyloid A concentration from 286.8 to 498.8 mug/mL, but did not affect other markers of inflammation including haptoglobin, fibrinogen, serum copper, or white blood cells. These results suggest that grain-induced SARA in mid lactation dairy cows increases the lysis of gram-negative bacteria and activates an inflammatory response.
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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 | 0.001 |
| 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".