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Record W2947628378 · doi:10.1139/cjm-2018-0399

Quantitative PCR enumeration of <i>vcgC</i> and 16S rRNA type A and B genes as virulence indicators for environmental and clinical strains of <i>Vibrio vulnificus</i> in Galveston Bay oysters

2019· article· en· W2947628378 on OpenAlexvenueno aff
Sedat Çam, Robin Brınkmeyer, John R. Schwarz

Bibliographic record

VenueCanadian Journal of Microbiology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsnot available
Fundersnot available
Keywords16S ribosomal RNAVibrio vulnificusBiologyVirulenceMicrobiologyRibosomal RNABayEnumerationStrain (injury)VibrioSalinityVibrionaceaeBacteriaGeneVeterinary medicineEcologyGenetics

Abstract

fetched live from OpenAlex

Oysters from a reef in Galveston Bay, Texas, USA, were screened for more virulent clinical strains versus less virulent environmental strains of Vibrio vulnificus using a combination of quantitative PCR assays for the virulence correlating gene (clinical variant, vcgC) and 16S rRNA types A and B (type A = environmental, type B = clinical). The combination of vcgC and 16S rRNA type B loci to determine clinical type strains was suitable, as indicated by the strong correlation (R 2 = 0.98; p &lt; 0.001) between these gene counts over time and their relative proportion (up to 93.8% and 94.3%, respectively) to vvhA genes used to quantify all strains of V. vulnificus. A strong seasonal shift of V. vulnificus strain types was observed. Environmental strains (16S rRNA type A) predominated from April to mid-June as salinities increased from 22 to 27 PSU (practical salinity unit) and temperatures rose 20 to 28 °C, with peak gene quantities of 16 812 ± 56 CFU/g. As temperatures increased to ≥30 °C from mid-June to September and salinities rose above 27 PSU, clinical strains (16S rRNA type B; vcgC) predominated with peak quantities 31 868 ± 287 and 32 360 ± 178 CFU/g, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.326

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 head0.012
GPT teacher head0.277
Teacher spread0.264 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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