Reproductive Tract Infections in Dairy Cows: Can Probiotics Curb Down the Incidence Rate?
Bibliographic record
Abstract
Postpartum uterine diseases are common in dairy cows and are a great concern for the dairy industry as they are associated with various consequences, including lower fertility, lower milk yield, and an overall negative impact on the host health. An infected uterus is a source of bacterial compounds and cytokines that spill into the systemic circulation, spreading inflammation to other organs. In this review article, we discuss a short overview of the anatomy of the reproductive tract of dairy cows and several infectious diseases of the uterus including metritis, endometritis, and pyometra. Additionally, we discuss the microbiome of the reproductive tract in health and during uterine diseases. As well, diagnostic criteria for metritis and endometritis and contributing factors for increased susceptibility to metritis infection are important topics of this review. To better understand how the uterus and reproductive tract respond to bacterial pathogens, a section of this review is dedicated to immunity of the reproductive tract. Both the innate and adaptive immunity systems are also discussed. We conclude the review with a factual discussion about the current treatments of uterine diseases and the new developments in the area of application of probiotics for uterine health. Mechanisms of actions of probiotics are discussed in detail and also some applications to prevent uterine infections in dairy cows are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".