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Record W1963522526 · doi:10.1038/srep05100

Impact of treatment strategies on cephalosporin and tetracycline resistance gene quantities in the bovine fecal metagenome

2014· article· en· W1963522526 on OpenAlexaff
Neena Kanwar, H.M. Scott, Bo Norby, Guy H. Loneragan, Javier Vinasco, Jennifer L. Cottell, Gabhan Chalmers, Muckatira M. Chengappa, Jianfa Bai, Patrick Boerlin

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Guelph
FundersCooperative State Research, Education, and Extension ServiceNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsCeftiofurTetracyclineChlortetracyclineFecesCephalosporinMicrobiologyBiologyAntibiotics

Abstract

fetched live from OpenAlex

The study objective was to determine the effects of two treatment regimens on quantities of ceftiofur and tetracycline resistance genes in feedlot cattle. The two regimens were ceftiofur crystalline-free acid (CCFA) administered to either one or all steers within a pen and subsequent feeding/not feeding of therapeutic doses of chlortetracycline. A 26-day randomized controlled field trial was conducted on 176 steers. Real-time PCR was used to quantify bla(CMY-2), bla(CTX-M), tet(A), tet(B), and 16S rRNA gene copies/gram of feces from community DNA. A significant increase in ceftiofur resistance and a decrease in tetracycline resistance elements were observed among the treatment groups in which all steers received CCFA treatment, expressed as gene copies/gram of feces. Subsequent chlortetracycline administration led to rapid expansion of both ceftiofur and tetracycline resistance gene copies/gram of feces. Our data suggest that chlortetracycline is contraindicated when attempting to avoid expansion of resistance to critically important third-generation cephalosporins.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.287
Teacher spread0.272 · 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

Citations59
Published2014
Admission routes1
Has abstractyes

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