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Record W2772373904 · doi:10.1002/bip.23091

Amphiphilic lysine conjugated to tobramycin synergizes legacy antibiotics against wild‐type and multidrug‐resistant <i>Pseudomonas aeruginosa</i>

2017· article· en· W2772373904 on OpenAlexafffundabout
Yinfeng Lyu, Ronald Domalaon, Xuan Yang, Frank Schweizer

Bibliographic record

VenuePeptide Science · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of Manitoba
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaManitoba Health Research Council
KeywordsTobramycinMicrobiologyPseudomonas aeruginosaAntibioticsChemistryAntimicrobialEffluxMultiple drug resistanceNovobiocinBiologyBiochemistryGentamicinBacteria

Abstract

fetched live from OpenAlex

Peptidomimetic modification is a common route of optimization for biologically active peptides. Previous studies in our group have shown that conjugation of amphiphilic tobramycin to other antibacterials enhance their latent outer membrane permeabilizing and efflux blocking activity toward Gram-negative pathogens including Pseudomonas aeruginosa. Herein, we describe the antimicrobial adjuvant properties of amphiphilic lysine ligated to tobramycin. The most potent amphiphilic lysine-tobramycin conjugate 3 potentiated the antibacterial efficacy of 8 clinically used antibiotics against wild type, multidrug- and extensively drug-resistant P. aeruginosa isolates from Canadian hospitals whereas amphiphilic lysine 4 did not. Antibiotics that are synergistic with conjugate 3 included moxifloxacin, ciprofloxacin, erythromycin, chloramphenicol, trimethoprim, novobiocin, linezolid, and fosfomycin. Out of these 8 antibiotics, novobiocin showed highest synergy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2017
Admission routes3
Has abstractyes

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