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Record W4307772450 · doi:10.1128/spectrum.02118-22

Detection of Diverse Sequence Types of Legionella pneumophila by Legiolert Enzymatic-Based Assay and the Development of a Long-Term Storage Protocol

2022· article· en· W4307772450 on OpenAlexafffund
Sara Matthews, Hana Trigui, Marianne Grimard-Conea, Elliston Vallarino Reyes, Gabriel Villiard, Dominique Charron, Émilie Bédard, Sébastien P. Faucher, Michèle Prévost

Bibliographic record

VenueMicrobiology Spectrum · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsUniversité de MontréalPolytechnique MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsLegionella pneumophilaLegionellaBiologyTypingLegionnaires' diseaseSubspeciesMicrobiologyOutbreakPopulationPotable waterBacteriaVirologyGeneticsEcologyMedicineEnvironmental science

Abstract

fetched live from OpenAlex

Legionnaires' disease is caused by the bacterium Legionella pneumophila, which can be found in a variety of water systems. When outbreaks of Legionnaires' disease occur, it is necessary to find the water systems transmitting the bacterium to humans. Access to historical isolates from water system samples is key for success in identifying sources but current regulations and isolation protocols mean very few isolates are obtained and stored long-term. We showed here that the Legiolert test could detect and produce isolates of a variety of L. pneumophila subspecies and types. In addition, the Legiolert test medium containing a representative population of isolates could be preserved for at least 12 months at -80°C with the addition of glycerol to the test medium. Therefore, we confirmed that the Legiolert method could be a useful tool for retrospective analysis of potential sources for an outbreak.

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)0.001
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 score0.004
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.260
Teacher spread0.247 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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