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Record W2905949372 · doi:10.3168/jds.2018-15272

Symposium review: Features of Staphylococcus aureus mastitis pathogenesis that guide vaccine development strategies

2018· review· en· W2905949372 on OpenAlexafffund
Julie Côté-Gravel, François Malouin

Bibliographic record

VenueJournal of Dairy Science · 2018
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Dairy CommissionDairy Farmers of Canada
KeywordsMastitisStaphylococcus aureusSubclinical infectionMedicineImmunologyImmune systemAntibioticsBiologyMicrobiologyVirologyBacteria

Abstract

fetched live from OpenAlex

Bovine mastitis affects animal health and welfare and milk production and quality, and it challenges the economic success of dairy farms. Staphylococcus aureus is one of the most commonly found pathogens in clinical mastitis but it also causes subclinical, persistent, and difficult-to-treat intramammary infections. Because of the failure of conventional antibiotic treatments and increasing pressure and concern from experts and consumers over the use of antibiotics in the dairy industry, many attempts have been made over the years to develop a vaccine for the prevention and control of Staph. aureus intramammary infections. Still, no commercially available vaccine formulation demonstrates sufficient protection and cost-effective potential. Multiple factors account for the lack of protection, including inadequate vaccine targets, high diversity among mastitis-provoking strains, cow-to-cow variation in immune response, and a failure to elicit an immune response that is appropriate for protection against a highly complex pathogen. The purpose of this review is to summarize key concepts related to the pathogenesis of Staph. aureus, and its interaction with the host, as well as to describe recent vaccine development strategies for prevention and control of Staph. aureus mastitis.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.050
GPT teacher head0.313
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations95
Published2018
Admission routes2
Has abstractyes

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