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Record W3123760462 · doi:10.3390/dairy2010004

Reproductive Tract Infections in Dairy Cows: Can Probiotics Curb Down the Incidence Rate?

2021· article· en· W3123760462 on OpenAlexafffund
Eduardo Barahona Rosales, Burim N. Ametaj

Bibliographic record

VenueDairy · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Alberta
FundersAlberta Livestock and Meat AgencyAgriculture Funding Consortium
KeywordsEndometritisMetritisUterusReproductive tractReproductive healthPhysiologyMedicineBiologyImmunologyGynecologyInternal medicinePregnancyLactationEnvironmental healthIce calvingPopulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.253
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2021
Admission routes2
Has abstractyes

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