Using Intravaginal Probiotics to Lower the Incidence of Uterine Infections and Improve Reproductive Performance and Productivity of Dairy Cows in Dairy Farms in Alberta
Bibliographic record
Abstract
Uterine infections are the number one reason for culling of cows in Canada and elsewhere because are associated with high incidence of infertility. Five hundred and twenty-six dairy cows (426 Holstein and 100 Jersey cows) from 4 dairy farms in Alberta (three farms had Holstein dairy cows and one farm had Jersey cows) were assigned into 3 experimental groups. In this study, we tested the effects of administrating a cocktail of lactic acid bacteria (LAB) in the vaginal tract of dairy cows on uterine health, reproductive performance, clinical diseases as well as milk production and composition. Overall, the results showed that the incidence rate of uterine infections by infusion of probiotics was decreased by 29%. The incidence of uterine infections in Farm A, C, and D (Holstein breed herds) decreased by 19%, 36%, and 21%, respectively. Interestingly, in Farm B (an organically managed Jersey herd) the incidence rate of uterine infections decreased by 50%. There was a difference in the incidence of uterine infections with regards to parity. Control cows that were administered skim milk (TRT1) and saline (TRT2) had higher odds of developing uterine infections compared to cows administered with probiotics (TRT3). The probiotic cocktail is composed of Lactobacillus sakei and two strains of Pediococcus acidilactici isolated from vaginal mucus of healthy pregnant Holstein cows. Results also showed that probiotics infused intravaginally lowered concentrations of glucose and cholesterol in the serum of both primiparous and multiparous cows diagnosed with uterine infections compared to TRT1 and TRT2 cows at +1 and +4 wks after parturition. In conclusion, intravaginal infusion of probiotics improved overall health status and increased milk production in multiparous dairy cows and modulated serum concentrations of glucose and cholesterol after parturition in dairy cows affected by uterine infections Implications: Around 45% of dairy cows are affected by metritis and 15% of the total number of cows registered with DHI Canada are culled because of reproductive issues totaling more than $100 million every year in Canada. A product such as intravaginal probiotics able to lower uterine infections might improve their overall health, productivity, and profitability of Canadian dairy industry.
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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 | 0.001 | 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) | 0.000 | 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".