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Record W2785541266 · doi:10.3168/jds.2017-13974

Cross-sectional study to identify staphylococcal species isolated from teat and inguinal skin of different-aged dairy heifers

2018· article· en· W2785541266 on OpenAlexaff
Pamela R. F. Adkins, Simon Dufour, J.N. Spain, Michael J. Calcutt, Thomas J. Reilly, George C. Stewart, John R. Middleton

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversité de Montréal
FundersNational Institute of Food and AgricultureUniversity of Missouri
KeywordsVeterinary medicineStaphylococcusStaphylococcus intermediusStaphylococcus xylosusBiologyStaphylococcus aureusStaphylococcus haemolyticusStreptococcus dysgalactiaeAnimal scienceMedicineStreptococcusGeneticsStreptococcus agalactiaeBacteria

Abstract

fetched live from OpenAlex

The purpose of this study was to describe the prevalence and distribution of staphylococcal species on the teat and inguinal skin of dairy heifers across the various stages of the heifer life cycle. The cross-sectional study included 106 Holstein heifers with an age range of 0 d to 27 mo that were selected from 11 different groups, based on housing type and age, on a single dairy operation. A composite swabbing sample including all 4 teats and a second composite sample including both inguinal regions of each heifer were collected using gas-sterilized electrostatic dusters (Swiffers; Procter and Gamble, Cincinnati, OH). Swabbing samples were mixed with 10 mL of sterile saline, agitated, and cultured on mannitol salt agar plates. At 24 h, plates were read and up to 10 staphylococcal colonies were saved for further analysis. Staphylococcal isolates were speciated using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or PCR amplification and partial sequencing of rpoB or tuf. The prevalence of staphylococci was compared between the inguinal and teat regions using the chi-squared or Fisher's exact test, as applicable. Logistic regression models were used to investigate the relationship between a heifer's age (treated as a quantitative continuous variable) and the probability of isolating a given staphylococcal species from a given body site (inguinal region or teats). Overall, the most common species identified were Staphylococcus haemolyticus followed by Staphylococcus chromogenes, Staphylococcus xylosus, Staphylococcus devriesei, and Staphylococcus sciuri. Staphylococcus aureus was more prevalent on the teat than in the inguinal region, whereas Staphylococcus arlettae was more prevalent in the inguinal region than on the teat. All other staphylococcal species were as likely to be found on the teat skin as the inguinal region skin. Isolation from the inguinal and teat skin was associated with age for Staphylococcus agnetis, S. chromogenes, S. devriesei, Staphylococcus equorum, S. haemolyticus, Staphylococcus lentus, S. sciuri, Staphylococcus vitulinus, and S. xylosus. The probability of finding S. chromogenes and S. agnetis on the teat and inguinal region increased with age, whereas the probability of S. devriesei and S. haemolyticus decreased with age. This study provides further insight into the ecology of staphylococcal species involved in heifer 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.313
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2018
Admission routes1
Has abstractyes

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