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Record W3211172340 · doi:10.3390/toxins13110755

Identification of Shiga-Toxin-Producing Shigella Infections in Travel and Non-Travel Related Cases in Alberta, Canada

2021· article· en· W3211172340 on OpenAlexaffabout
Shuai Zhi, Brendon Parsons, Jonas Szelewicki, Yue T. K. Yuen, Patrick Fach, Sabine Delannoy, Vincent Li, Christina Ferrato, Stephen B. Freedman, Bonita E. Lee, Linda Chui

Bibliographic record

VenueToxins · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsStollery Children's HospitalAlberta Children's HospitalProvincial Laboratory of Public HealthWomen and Children’s Health Research InstituteUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsShigellaShiga toxinSerotypeShigella flexneriPhylogenetic treeBiologyShigella dysenteriaeCladeShigellosisMicrobiologyVirologyEscherichia coliGeneGenetics

Abstract

fetched live from OpenAlex

It has long been accepted that Shiga toxin (Stx) only exists in Shigella dysenteriae serotype 1. However, in recent decades, the presence of Shiga toxin genes (stx) in other Shigella spp. have been reported. We screened 366 Shigella flexneri strains from Alberta, Canada (2003 to 2016) for stx and 26 positive strains were identified. These isolates are highly related with the majority originating from the Dominican Republic and three isolates with Haiti origin. Both phylogenetic and spanning tree analysis of the 26 Alberta and 29 stx positive S. flexneri originating from the U.S., France, Canada (Quebec) and Haiti suggests that there are geographic specific distribution patterns (Haiti and Dominican Republic clades). This study provides the first comprehensive whole genome based phylogenetic analysis of stx positive S. flexneri strains as well as their global transmission, which signify the public health risks of global spreading of these strains.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.260
Teacher spread0.250 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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