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Characterization of an extremely heat-resistant Escherichia coli obtained from a beef processing facility

2011· article· en· W1589757328 on OpenAlexafffund
Elena Dlusskaya, Lynn M. McMullen, Michael G. Gänzle

Bibliographic record

VenueJournal of Applied Microbiology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsUniversity of Alberta
FundersCanada Research Chairs
KeywordsEscherichia coliLactic acidFood scienceBiologyFood microbiologyStrain (injury)BacteriaMicrobiologyBeef cattleVeterinary medicineAnimal scienceMedicineBiochemistry

Abstract

fetched live from OpenAlex

AIMS: This study aimed to determine the survival of Escherichia coli strains during steam and lactic acid decontamination interventions currently used by the beef-processing industry, and to determine their heat resistance. METHODS AND RESULTS: Strains were grouped into cocktails of five strains each differing in their RAPD patterns for subsequent identification. Steam and lactic acid treatments on meat reduced cell counts of E. coli strain cocktails by 90-99%. The 20 slaughter plant isolates exhibited only minor variation in their resistance to steam and lactic acid treatments but were more resistant than reference strains (three strains) or isolates from live cattle (seven strains). D(60) values of strains from live cattle, and reference strains ranged from 0·1 to 0·5 min, in keeping with literature data. However, D(60) values of current slaughter plant isolates ranged between 15 for E. coli DM18.3 and 71 min AW 1.7. Cell counts of E. coli AW 1.7 were reduced by <5 log(10) CFU g(-1) in ground beef patties cooked to an internal temperature of 71°C. CONCLUSIONS: Strains of E. coli that survive cooking of ground beef to the recommended internal temperature of 71°C can be isolated from beef-processing facilities. SIGNIFICANCE AND IMPACT OF THE STUDY: Pathogen interventions in current commercial beef slaughter may select for extremely heat-resistant strains of E. coli.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.658

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 head0.043
GPT teacher head0.258
Teacher spread0.216 · 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

Citations95
Published2011
Admission routes2
Has abstractyes

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