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Record W4285613396 · doi:10.3934/microbiol.2022022

Evaluation of the use of CRISPR loci for discrimination of <i>Salmonella enterica</i> subsp. <i>enterica</i> serovar Enteritidis strains recovered in Canada and comparison with other subtyping methods

2022· article· en· W4285613396 on OpenAlexaffabout
Susan A. Nadin‐Davis, L. S. Pope, John Devenish, Ray Allain, Dele Ogunremi

Bibliographic record

VenueAIMS Microbiology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsSubtypingBiologySalmonella entericaCRISPRTypingPulsed-field gel electrophoresisGeneticsVirulenceSalmonella enteritidisSerotypeComputational biologyOutbreakGenomeSalmonellaMicrobiologyGeneVirologyGenotypeBacteria

Abstract

fetched live from OpenAlex

Salmonella enterica subsp. enterica serovar Enteritidis remains one of the most important foodborne pathogens worldwide. To minimise its public health impact when outbreaks of the disease occur, timely investigation to identify and recall the contaminated food source is necessary. Central to this approach is the need for rapid and accurate identification of the bacterial subtype epidemiologically linked to the outbreak. While traditional methods of S. Enteritidis subtyping, such as pulsed field gel electrophoresis (PFGE) and phage typing (PT), have played an important role, the clonal nature of this organism has spurred efforts to improve subtyping resolution and timeliness through molecular based approaches. This study uses a cohort of 92 samples, recovered from a variety of sources, to compare these two traditional methods for S. Enteritidis subtyping with recently developed molecular techniques. These latter methods include the characterisation of two clustered regularly interspaced short palindromic repeats (CRISPR) loci, either in isolation or together with sequence analysis of virulence genes such as fimH. For comparison, another molecular technique developed in this laboratory involved the scoring of 60 informative single nucleotide polymorphisms (SNPs) distributed throughout the genome. Based on both the number of subtypes identified and Simpson's index of diversity, the CRISPR method was the least discriminatory and not significantly improved with the inclusion of fimH gene sequencing. While PT analysis identified the most subtypes, the SNP-PCR process generated the greatest index of diversity value. Combining methods consistently improved the number of subtypes identified, with the SNP/CRISPR typing scheme generating a level of diversity comparable with that of PT/PFGE. While these molecular methods, when combined, may have significant utility in real-world situations, this study suggests that CRISPR analysis alone lacks the discriminatory capability required to support investigations of foodborne disease outbreaks.

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)0.002
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 score0.972
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.071
GPT teacher head0.289
Teacher spread0.218 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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