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Use of a Multiplex Molecular Beacon Platform for Rapid Detection of Methicillin and Vancomycin Resistance in <i>Staphylococcus aureus</i>

2005· article· en· W2109427599 on OpenAlexaff
Daniel Sinsimer, Surbhi Leekha, Steven Park, Salvatore A. E. Marras, Larry Koreen, Barbara Willey, Steve Naidich, Kimberlee A. Musser, Barry N. Kreiswirth

Bibliographic record

VenueJournal of Clinical Microbiology · 2005
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsMount Sinai Hospital
FundersU.S. Food and Drug Administration
KeywordsVirulenceStaphylococcus aureusVancomycinMicrobiologySCCmecPanton–Valentine leukocidinDrug resistanceMultiplexMethicillin-resistant Staphylococcus aureusAntibiotic resistanceLeukocidinMultiplex polymerase chain reactionBiologyAntibioticsVirulence factorMolecular beaconVirologyGeneBacteriaPolymerase chain reactionBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Drug resistance, particularly vancomycin and methicillin resistance, in Staphylococcus aureus continues to emerge as a significant public health threat in both the hospital and community settings. In addition to the limited treatment options, S. aureus strains acquire and express numerous virulence factors that continue to increase its ability to cause a wide spectrum of human disease. As a result, empirical treatment decisions are confounded and there is a heightened need for a diagnostic test (or assay) to rapidly identify antibiotic resistance and specific virulence determinants and indicate the appropriate treatment. To that end we developed a platform using multiplex molecular beacon probes with real-time PCR for the rapid detection of drug resistance-determining genes and virulence factors in S. aureus. In this study, we demonstrate the specificity and sensitivity of our platform for detection of the genes conferring methicillin (mecA) and vancomycin (vanA) resistance as well as a gene encoding the virulence factor Panton-Valentine leucocidin (lukF) in S. aureus isolates.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.092
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.064
GPT teacher head0.362
Teacher spread0.298 · 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

Citations39
Published2005
Admission routes1
Has abstractyes

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