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Record W2766127091 · doi:10.1128/aac.01904-17

<i>In Vitro</i> Activity of Meropenem-Vaborbactam against Clinical Isolates of KPC-Positive Enterobacteriaceae

2017· article· en· W2766127091 on OpenAlexaff
Meredith Hackel, Olga Lomovskaya, Michael N. Dudley, James A. Karlowsky, Daniel F. Sahm

Bibliographic record

VenueAntimicrobial Agents and Chemotherapy · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Manitoba
FundersDepartment of Human Services
KeywordsEnterobacteriaceaeMeropenemMicrobiologyBroth microdilutionMedicineBiologyMinimum inhibitory concentrationAntibioticsEscherichia coliAntibiotic resistanceGeneticsGene

Abstract

fetched live from OpenAlex

ABSTRACT Vaborbactam (formerly RPX7009) is a novel inhibitor of serine β-lactamases, including Ambler class A carbapenemases, such as KPCs. The current study evaluated the in vitro activity of the combination agent meropenem-vaborbactam against a global collection of 991 isolates of KPC-positive Enterobacteriaceae collected in 2014 and 2015 using the Clinical and Laboratory Standards Institute (CLSI) standard broth microdilution method. The MIC 90 of meropenem (when tested with a fixed concentration of 8 μg/ml of vaborbactam) for isolates of KPC-positive Enterobacteriaceae was 1 μg/ml, and MIC values ranged from ≤0.03 to &gt;32 μg/ml; 99.0% (981/991) of isolates had meropenem-vaborbactam MICs of ≤4 μg/ml, the U.S. FDA-approved MIC breakpoint for susceptibility to meropenem-vaborbactam (Vabomere). Vaborbactam lowered the meropenem MIC 50 from 32 to 0.06 μg/ml and the MIC 90 from &gt;32 to 1 μg/ml. There were no differences in the activity of meropenem-vaborbactam when the isolates were stratified by KPC variant type. We conclude that meropenem-vaborbactam demonstrates potent in vitro activity against a worldwide collection of clinical isolates of KPC-positive Enterobacteriaceae collected in 2014 and 2015.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.664

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 head0.013
GPT teacher head0.296
Teacher spread0.283 · 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

Citations125
Published2017
Admission routes1
Has abstractyes

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