Feed restriction to induce and meloxicam to mitigate potential systemic inflammation in dairy cows before calving
Bibliographic record
Abstract
Most dairy cows experience a transient decrease in feed intake in the 1 to 2 wk before calving, which has been associated with systemic inflammation (SI), indicated by increased blood haptoglobin (Hp) concentration. We aimed to characterize the association between prepartum decrease in feed intake and the onset of SI and, if present, the ability of meloxicam (MEL), a non-steroidal anti-inflammatory drug, to mitigate SI. Holstein cows (n = 45) were assigned to control (n = 13), feed restriction (FR) untreated (FR-U; n = 15), and FR treated with MEL (FR-T; n = 17) groups. Daily feed intake was measured from -22 d from expected parturition until 35 d postpartum. Control cows were fed ad libitum, whereas FR-U and FR-T cows were reduced to 60% of their average intake for 4 consecutive days (-15 to -12 d from expected calving). The FR-T cows received MEL (0.5 mg/kg of body weight) once daily for 4 consecutive days (-13 to -10 d from expected calving). Blood samples were collected -22, -15, -14, -13, -12, -10, -7, -5, -3, 0, 1, 3, 5, 7, 15, 22, and 35 d relative to calving to measure serum concentrations of total calcium, total protein, albumin, globulin, cholesterol, urea, glucose, gamma-glutamyl transferase, aspartate aminotransferase, glutamate dehydrogenase, β-hydroxybutyrate, nonesterified fatty acids, Hp, and insulin-like growth factor-1. Serum concentrations of lipopolysaccharide-binding protein were measured -22, -15, -14, -13, -12, and -10 d from expected calving. Simplified glucose tolerance tests were performed on -15, -12, -5, 1, and 5 d relative to calving. Mixed linear regression models were used to assess the effects of FR and MEL on each metabolite. The interaction between treatment group and blood sampling day was forced into each model. All models accounted for body condition score, parity, and the cow as a random effect. Nonesterified fatty acids concentrations in both the FR-U and FR-T groups significantly increased from the second until the last day of FR. Feed restriction increased urea concentrations compared with the control group on -14 d but decreased urea concentrations on -10 d from expected calving. Control cows had greater β-hydroxybutyrate concentrations compared with FR cows on 15, 21, and 35 d postpartum. For all other metabolites, no differences were found. This model of FR produced substantial fat mobilization but based on serum Hp and lipopolysaccharide-binding protein concentrations did not generate measurable SI; therefore, we were unable to evaluate the ability of MEL to mitigate SI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".