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Record W3007669200 · doi:10.1002/adbi.201900242

Electroceutical Silk–Silver Gel to Eradicate Bacterial Infection

2020· article· en· W3007669200 on OpenAlexaff
Daniela Vieira, Samuel Angel, Yazan Honjol, Samantha Gruenheid, Uwe Gbureck, Edward J. Harvey, Géraldine Merle

Bibliographic record

VenueAdvanced Biosystems · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsPolytechnique MontréalMcGill University
Fundersnot available
KeywordsSILKMicrobiologyMaterials scienceBiologyComposite material

Abstract

fetched live from OpenAlex

Abstract With more than 50% of bacteria resistant to standard antibiotics, new strategies to treat bacterial infection and colonization are needed. Based on the concept of targeting the bacteria synergistically on various fronts, it is hypothesized that an electrical insult associated with antibacterial materials may be a highly effective means of killing bacteria. In this work, an injectable conductive gel based on silk fibroin (SF) and silver nanoparticles (Ag‐NPs) is synthesized, capable of coating a zone of injury, allowing the application of a low electrical current to decrease bacterial contamination. With a high conductivity of 1.5 S cm −1 , SF/Ag‐NPs gels killed 80% of Escherichia coli in 1 min, no toxicity toward Chinese hamster ovary cells is observed. The mechanism of an electrical composite gel combined with electrical wound therapy is associated with silver ion (Ag + ) release, and reactive oxygen species (ROS) production. The findings in the present study show a similar Ag + release for treatment with gels and the combined effect, whereas ROS generation is 50% higher when a small electrical current is applied leading to a broad bactericidal effect.

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 score0.080
Threshold uncertainty score0.998

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)0.0000.002

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 head0.267
Teacher spread0.248 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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