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Record W1970284476 · doi:10.1080/15321810701454821

Detection of<i>Eschericia coli</i>O157:H7 by Fluorescence Polarization Assay and Polymerase Chain Reaction

2007· article· en· W1970284476 on OpenAlexaff
Klaus Nielsen, Phillip D. Smith, H. McRae, Yu Wang, J. Widdison

Bibliographic record

VenueJournal of Immunoassay and Immunochemistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsPolymerase chain reactionMicrobiologyEscherichia coliMultiplex polymerase chain reactionSerotypeSerologyImmunoassayBiologyImmunomagnetic separationShiga toxinPopulationMultiplexVirologyMolecular biologyChemistryAntibodyMedicineGenetics

Abstract

fetched live from OpenAlex

It is recognized that cattle and other domestic animals can be a reservoir of pathogenic Escherichia coli, including serotype O157:H7. To contain this potential health hazard, the first step is the identification of the carrier animals. For these purposes, a rapid serological screening test, a fluorescence polarization assay (FPA) was developed and results obtained from a randomly selected cattle population as well as cattle immunized with E. coli O157:H7 were compared to those obtained with an indirect enzyme immunoassay (IELISA). To identify pathogenic strains in carrier animals, polymerase chain reactions (PCR) for Shiga-like toxins I and II were implemented using agarose electrophoresis. The sensitivity of the fecal extracted E. coli for Shiga-like toxin I and II was approximately 200 CFU per reaction using multiplex hot-start nested PCR. The sensitivity of the fecal extracted E. coli varied from approximately 5x10(2) to 2.5x10(3) CFU per reaction depending on the commercial kits used. The combination of the serological screening FPA and hot-start nested PCR confirmatory assays provided rapid identification of the pathogen.

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)0.001
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 score0.018
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.004
GPT teacher head0.235
Teacher spread0.231 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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