Effects of feed restriction and prolactin-release inhibition at drying-off on susceptibility to new intramammary infection in cows
Bibliographic record
Abstract
A cow's risk of acquiring a new intramammary infection during the dry period increases with milk production at drying-off. A method commonly used to reduce milk production is a drastic reduction in feed supply in the days that precede drying-off. Milk production can also be reduced by inhibiting the lactogenic signal driven by prolactin (PRL). This study aimed to compare the effects of these 2 drying-off procedures on milk production, metabolism, and susceptibility to intramammary infection in cows. A total of 21 Holstein cows in late lactation were assigned to 1 of 3 treatments based on milk yield, somatic cell count, and parity. The cows were fed a lactation diet until drying-off (control), only dry hay during the last 5 d before drying-off (DH), or the same diet as the control cows but with twice-daily i.m. injections of 4mg of quinagolide, a specific inhibitor of PRL release, from 5 d before drying-off until 13 d after (QN). On d 1 to 7 after the last milking, the cows were challenged by daily teat dipping in a solution containing Streptococcus agalactiae at 5×10(7) cfu/mL. Quinagolide induced a decrease in PRL concentration in blood on all the injection days. Blood PRL was also depressed in the hay-fed cows before drying-off. Both the QN and DH treatments induced a decrease in milk production, which at drying-off averaged 12.0, 10.0, and 21.7kg/d for the QN, DH, and control cows, respectively. The DH treatment decreased blood concentration of glucose and increased blood concentrations of β-hydroxybutyrate and nonesterified fatty acids before drying-off. Somatic cell count at drying-off was greater in the milk of the QN cows than in that of the control cows but after drying-off was greater in the mammary secretions of the control cows than in those of the QN cows. The number of S. agalactiae colonies found in mammary secretions on d 8 and 14 after the last milking was lower for the QN cows than for the control cows. The percentage of S. agalactiae-infected quarters was also lower in the QN cows than in the control cows and on d 14 averaged 17.2, 33.7, and 57.5% in the QN, DH, and control cows, respectively. No differences between the DH and control groups were observed for either bacterial count or infection rate. In conclusion, this experiment shows that PRL-release inhibition could be an alternative for reducing milk production and improving resistance to intramammary infection at drying-off.
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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) | 0.001 | 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".