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Record W2010649074 · doi:10.1139/w03-048

Magnetic bead hybridization to detect enterotoxigenic<i>Escherichia coli</i>strains associated with cattle in environmental water sources

2003· article· en· W2010649074 on OpenAlexvenueno aff
Y L Tsai, Joanne Y. Le, Betty H. Olson

Bibliographic record

VenueCanadian Journal of Microbiology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsPolymerase chain reactionEscherichia coliEnterotoxigenic Escherichia coliBiologyDetection limitOligomer restrictionReal-time polymerase chain reactionEnterotoxinMolecular biologyDNAMicrobiologyOligonucleotideImmunomagnetic separationGeneChemistryChromatographyBiochemistry

Abstract

fetched live from OpenAlex

A magnetic capture hybridization - polymerase chain reaction (MCH-PCR) method was used to increase the detection sensitivity of the enterotoxin gene LTIIa, used as a biomarker for waste in environmental samples. The samples were collected from cow lagoons of different farms and from environmental waters. Total DNA was extracted from colonies grown on mTEC medium or directly from environmental samples. The cow-specific Escherichia coli LTIIa gene was used as a DNA marker. A LTIIa-specific oligonucleotide probe was designed to capture the LTIIa marker during the MCH, followed by PCR. Varying levels of humic acid were added to the DNA extracts to evaluate the sensitivity and effectiveness of MCH-PCR. The minimal detection limit of MCH-PCR for the LTIIa gene was 2.5 ag/muL DNA. In the presence of humic acid, MCH-PCR was able to increase the detection sensitivity 10 000-fold over that of conventional PCR. The MCH-PCR could also detect one cell with the LTIIa DNA marker in a 1-L seeded environmental water sample. Results in this study indicate that MCH-PCR is more sensitive than nested PCR in testing environmental samples.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.999

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)0.0020.000

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 head0.006
GPT teacher head0.160
Teacher spread0.154 · 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.

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

Citations19
Published2003
Admission routes1
Has abstractyes

Explore more

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