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Record W2406813715 · doi:10.1021/acs.biomac.5b01207

Antimicrobial Organometallic Dendrimers with Tunable Activity against Multidrug-Resistant Bacteria

2015· article· en· W2406813715 on OpenAlexafffund
Alaa S. Abd‐El‐Aziz, Christian Agatemor, Nola Etkin, David P. Overy, Martin Lanteigne, Katherine McQuillan, Russell G. Kerr

Bibliographic record

VenueBiomacromolecules · 2015
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAntimicrobialDendrimerMicrobiologyBacteriaMultiple drug resistanceEnterococcus faeciumChemistryStaphylococcus aureusBiologyBiochemistryDrug resistanceAntibiotics

Abstract

fetched live from OpenAlex

Multidrug-resistant pathogens are an increasing threat to public health. In an effort to curb the virulence of these pathogens, new antimicrobial agents are sought. Here we report a new class of antimicrobial organometallic dendrimers with tunable activity against multidrug-resistant Gram-positive bacteria that included methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus faecium. Mechanistically, these redox-active, cationic organometallic dendrimers induced oxidative stress on bacteria and also disrupted the microbial cell membrane. The minimum inhibitory concentrations, which provide a quantitative measure of the antimicrobial activity of these dendrimers, were in the low micromolar range. AlamarBlue cell viability assay also confirms the antimicrobial activity of these dendrimers. Interestingly, these dendrimers were noncytotoxic to epidermal cell lines and to mammalian red blood cells, making them potential antimicrobial platforms for topical applications.

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 score0.000
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)0.0000.000

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations74
Published2015
Admission routes2
Has abstractyes

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