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Digoxigenin-labelled peptide nucleic acid to detect lactobacilli PCR amplicons immobilized on membranes from denaturing gradient gel electrophoresis

2003· article· en· W2092843650 on OpenAlexaff
Jeremy P. Burton, John K. McCormick, Peter A. Cadieux, Gregor Reid

Bibliographic record

VenueLetters in Applied Microbiology · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsTemperature gradient gel electrophoresisAmpliconPeptide nucleic acidDigoxigeninBiologyMolecular biologyNucleic acidGel electrophoresisLactobacillusSouthern blot16S ribosomal RNANucleic acid thermodynamicsHybridization probeDNABiochemistryBacteriaPolymerase chain reactionGeneRNAGene expressionGenetics

Abstract

fetched live from OpenAlex

AIMS: To develop a digoxigenin (DIG)-labeled peptide nucleic acid (PNA) probe for the detection of Lactobacillus-related genera amongst eubacterial amplicons obtained from vaginal samples using denaturing gradient gel electrophoresis (DGGE) blots. METHODS AND RESULTS: Part of the 16S rRNA gene sequence was used as a target for the PNA probe. After confirming probe specificity using chromosomal DNA from species and isolates that have been detected in the urogenital tract, it was successfully used to detect lactobacilli amplicons generated using eubacterial-specific 16S rRNA gene-targeted primers from vaginal tract samples immobilized on membranes from DGGE. CONCLUSIONS: The Lactobacillus-specific PNA probe could distinguish between DNA fragments from lactobacilli in a DGGE gel from other bacterial species, including those that migrated to a similar position. SIGNIFICANCE AND IMPACT OF THE STUDY: The use of the DIG-labelled PNA probe on blots of eubacterial PCR products from DGGE gels can be used to specifically detect lactobacilli in complex vaginal 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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 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

Citations9
Published2003
Admission routes1
Has abstractyes

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