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Record W1977214846 · doi:10.1139/w09-060

A refrigeration temperature of 4 °C does not prevent static growth of <i>Yersinia pestis</i> in heart infusion broth

2009· article· en· W1977214846 on OpenAlexvenueno aff
Stephen D. Torosian, Patrick M. Regan, Tara Doran, Michael A. Taylor, Aaron B. Margolin

Bibliographic record

VenueCanadian Journal of Microbiology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsnot available
Fundersnot available
KeywordsYersinia pestisDoubling timeMicrobiologyFood scienceBacterial growthRefrigerationStrain (injury)BiologyPathogenBacteriaVirulenceIn vitroBiochemistryGenetics

Abstract

fetched live from OpenAlex

Multiple barriers such as inspections, testing, and proper storage conditions are used to minimize the risk of contaminated food. Knowledge of which barriers, such as refrigeration, are effective in preventing pathogen growth and persistence, can help direct the focus of efforts during food sampling. In this study, the doubling times were evaluated for 10 strains of Yersinia pestis of different genetic background cultured in heart infusion broth (HIB) kept at 4 degrees C +/- 1 degrees C under static conditions. Nine out of the 10 strains were able to grow at 4 degrees C +/- 1 degrees C. Apparent doubling times for 7 of the strains ranged from 41 to 50 h. Strain Harbin and strain D1 had apparent doubling times of 65 and 35 h, respectively, and strain O19 Ca-6 did not grow at all. Analysis of variance showed that the averaged growth data (colony forming units per mL) between strains that grew were not significantly different. The data presented here demonstrate that refrigeration alone is not an effective barrier to prevent static growth of Y. pestis in HIB. These findings provide the preliminary impetus to investigate Y. pestis growth in a variety of food matrices that may provide a similar environment as HIB.

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 score0.036
Threshold uncertainty score0.563

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 head0.005
GPT teacher head0.238
Teacher spread0.233 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of MicrobiologySame topicYersinia bacterium, plague, ectoparasites researchFrench-language works237,207