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Record W2616421542 · doi:10.3168/jds.2016-12478

Prevalence of non-aureus staphylococci species causing intramammary infections in Canadian dairy herds

2017· article· en· W2616421542 on OpenAlexafffundabout
Larissa Anuska Zeni Condas, Jeroen De Buck, Diego B. Nóbrega, Domonique A. Carson, Sohail Naushad, Sarne De Vliegher, Ruth N. Zadoks, John R. Middleton, Simon Dufour, John P. Kastelic, Herman W. Barkema

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversité de MontréalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNovalaitUniversité de MontréalPublic Health AgencyDairy Farmers of OntarioAgriculture and Agri-Food CanadaDairy Farmers of Nova ScotiaPublic Health Agency of CanadaDairy Farmers of Canada
KeywordsMastitisBiologyStaphylococcus aureusHerdStaphylococcus epidermidisVeterinary medicineStaphylococcus xylosusUdderMicrobiologyStaphylococcusStreptococcus dysgalactiaeMedicineAnimal scienceStreptococcus agalactiaeBacteriaStreptococcusGenetics

Abstract

fetched live from OpenAlex

Non-aureus staphylococci (NAS), the microorganisms most frequently isolated from bovine milk worldwide, are a heterogeneous group of numerous species. To establish their importance as a group, the distribution of individual species needs to be determined. In the present study, NAS intramammary infection (IMI) was defined as a milk sample containing ≥1,000 cfu/mL in pure or mixed culture that was obtained from a cohort of cows assembled by the Canadian Bovine Mastitis Research Network. Overall, 6,213 (6.3%) of 98,233 quarter-milk samples from 5,149 cows and 20,305 udder quarters were associated with an NAS IMI. Of the 6,213 phenotypically identified NAS isolates, 5,509 (89%) were stored by the Canadian Bovine Mastitis Research Network Mastitis Pathogen Collection and characterized using partial sequencing of the rpoB housekeeping gene, confirming 5,434 isolates as NAS. Prevalence of each NAS species IMI was estimated using Bayesian models, with presence of a specific NAS species as the outcome. Overall quarter-level NAS IMI prevalence was 26%. The most prevalent species causing IMI were Staphylococcus chromogenes (13%), Staphylococcus simulans (4%), Staphylococcus haemolyticus (3%), Staphylococcus xylosus (2%), and Staphylococcus epidermidis (1%). The prevalence of NAS IMI as a group was highest in first-parity heifers and was evenly distributed throughout cows in parities ≥2. The IMI prevalence of some species such as S. chromogenes, S. simulans, and S. epidermidis differed among parities. Overall prevalence of NAS IMI was 35% at calving, decreased over the next 10 d, and then gradually increased until the end of lactation. The prevalence of S. chromogenes, Staphylococcus gallinarum, Staphylococcus cohnii, and Staphylococcus capitis was highest at calving, whereas the prevalence of S. chromogenes, S. haemolyticus, S. xylosus, and S. cohnii increased during lactation. Although the overall prevalence of NAS IMI was similar across barn types, the prevalence of S. simulans, S. xylosus, S. cohnii, Staphylococcus saprophyticus, S. capitis, and Staphylococcus arlettae IMI was higher in tiestall barns; the prevalence of S. epidermidis IMI was lowest; and the prevalence of S. chromogenes and Staphylococcus sciuri IMI was highest in bedded-pack barns. Staphylococcus simulans, S. epidermidis, S. xylosus, and S. cohnii IMI were more prevalent in herds with intermediate to high bulk milk somatic cell count (BMSCC) and S. haemolyticus IMI was more prevalent in herds with high BMSCC, whereas other common NAS species IMI were equally prevalent in all 3 BMSCC categories. Distribution of NAS species IMI differed among the 4 regions of Canada. In conclusion, distribution differed considerably among NAS species IMI; therefore, accurate identification (species level) is essential for studying NAS epidemiology.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.274
Teacher spread0.245 · 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

Citations107
Published2017
Admission routes3
Has abstractyes

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