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Record W2317868866 · doi:10.1021/la200102z

Bacterial Capture Efficiency and Antimicrobial Activity of Phage-Functionalized Model Surfaces

2011· article· en· W2317868866 on OpenAlexafffund
Zeinab Hosseinidoust, Theo G. M. van de Ven, Nathalie Tufenkji

Bibliographic record

VenueLangmuir · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsMcGill University
FundersUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsAntimicrobialBacteriophageBacteriaEscherichia coliSalmonellaBiofilmChemistryMicrobiologyMembraneBiophysicsNanotechnologyMaterials scienceBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

The rise of antibiotic-resistant bacteria has directed substantial attention toward the use of bacteriophages as a means to control bacterial populations. It has been proposed that bacteriophages can be applied as a coating on surfaces in healthcare settings or on indwelling medical devices to create an antimicrobial surface. In this study, antimicrobial model surfaces functionalized with five different types of bacteriophage were prepared and characterized with X-ray photoelectron spectroscopy and atomic force microscopy. The bacterial capture efficiency of these functionalized surfaces was studied for two common bacteria, Escherichia coli and Salmonella typhimurium. Binding of the phages to a solid surface affected their biofunctionality as expressed by the capture efficiency and rate of host membrane disruption. Moreover, the size and shape of the bacteriophage and positioning of its specific binding proteins significantly affected its bacterial capture capability in the immobilized state. Symmetric bacteriophages were found to be a better choice for antibacterial surfaces compared to more asymmetric tailed bacteriophages. Immobilized phages were found to disrupt the membranes of attached bacteria and are thus proposed as a candidate for antimicrobial surfaces.

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 categoriesInsufficient 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 score0.471
Threshold uncertainty score0.997

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)0.0040.000

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 head0.218
Teacher spread0.201 · 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

Citations76
Published2011
Admission routes2
Has abstractyes

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