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Record W3013766223 · doi:10.1186/s12985-019-1269-7

Effect of a bacteriophage T5virus on growth of Shiga toxigenic Escherichia coli and Salmonella strains in individual and mixed cultures

2020· article· en· W3013766223 on OpenAlexafffund
Yan D. Niu, Hui Liu, Roger P. Johnson, Tim A. McAllister, Kim Stanford

Bibliographic record

VenueVirology Journal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsPublic Health Agency of CanadaAgriculture Food and Rural DevelopmentAgriculture and Agri-Food CanadaUniversity of Calgary
FundersChina Scholarship CouncilAgriculture and Agri-Food CanadaAlberta Livestock and Meat AgencyNatural Sciences and Engineering Research Council of CanadaMinistry of Education, IndiaGovernment of AlbertaAlberta Agriculture and ForestryPublic Health AgencyPublic Health Agency of Canada
KeywordsSalmonellaMicrobiologyBiologySalmonella entericaSerotypeBacteriophageEscherichia coliMultiplicity of infectionVirologyPhage typingBacteriaVirusGene

Abstract

fetched live from OpenAlex

A previously isolated a bacteriophage, vB_EcoS_AKFV33 of T5virus, demonstrated great potential in biocontrol of Shiga toxigenic Escherichia coli (STEC) O157. This study further evaluated its potential as a biocontrol agent in broth culture against other important non-O157 serogroups of STEC and Salmonella. AKFV33 was capable of lysing isolates of STEC serogroups O26 (n = 1), O145 (n = 1) and Salmonella enterica serovars (n = 6). In a broth culture microplate system, efficacy of AKFV33 for killing STEC O26:H11, O145:NM and Salmonella was improved (P < 0.05) at a lower multiplicity of infection and sampling time (6-10 h), when STEC O157:H7 was also included in the culture. This phage was able to simultaneously reduce numbers of STEC and Salmonella in mixtures with enhanced activity (P < 0.05) against O157:H7 and O26:H11, offering great promise for control of multiple zoonotic pathogens at both pre and post-harvest.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 designBench or experimental
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

Citations18
Published2020
Admission routes2
Has abstractyes

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