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Record W3092623520 · doi:10.26685/urncst.198

Potential Application of Silver Nanoparticles Encapsulated in Branched Amphiphilic Peptide Capsules to Circumvent Antibiotic Resistance in Methicillin-Resistant Staphylococcus Aureus: A Research Protocol

2020· article· en· W3092623520 on OpenAlexafffundabout
Russell Leong, Zejia Chen, Chelsea Zhang

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsAntibioticsStaphylococcus aureusIn vivoAntibiotic resistanceMethicillin-resistant Staphylococcus aureusEx vivoMinimum inhibitory concentrationMedicineMicrobiologyPharmacologyChemistryBacteriaBiologyBiotechnology

Abstract

fetched live from OpenAlex

Introduction: Antibiotic resistance is a major global health concern as many fatalities occur each year due to severe infections associated with it, such as sepsis. This research protocol hopes to address the issue of antibiotic resistance in methicillin-resistant Staphylococcus aureus (MRSA) by using an alternative antibiotic therapy. Silver nanoparticles (AgNPs), which have antibiotic properties and low antibiotic resistance potential, will be administered in conjunction with stable and biocompatible branched amphiphilic peptide capsules (BAPCs). Surface functionalized ligands will be attached to the peptide capsules to target iron receptors essential to MRSA survival. By targeting its iron receptors, the bacteria will be unable to mutate in such a way that would attenuate its uptake of this antibiotic. Methods: Testing will begin in vitro on MRSA cultures to assess the minimum inhibitory concentration of the various AgNP treatment groups. Constraints determining the minimum inhibitory concentration include a minimum 3-hour delay in bactericidal effect from application, and a 95% eradication efficacy. Drug efficacy will be evaluated via ex vivo and in vivo mice experiments, with off-target toxicity effects measured via BAPC targeted immunohistochemistry, and both serological and organ-based tests. Repeated in vivo treatments will be performed against a control to measure the relative antibiotic resistance advantages this treatment proposes. As this protocol requires the use of mice, ethics approval from the Canadian Council on Animal Care will be obtained prior to experimentation. Results: It is expected that the AgNPs will be successfully encapsulated within the structurally-sound BAPCs and that the AgNPs will eradicate a significant amount of the MRSA present in the human body with minimal side effects. The in vitro, ex vivo and in vivo tests are likely to yield results that demonstrate that the most effective protocol in eradicating MRSA is to use AgNPs in BAPCs in conjunction with a vancomycin treatment. The proposal will likely satisfy the criteria for appropriate AgNP doses, which includes bactericidal effect in a time frame of hours, over 95% efficacy, and statistically significant reproducibility. Discussion: Successful BAPC mediated delivery of AgNPs prove the potential for targeted antibiotic application against multidrug bacteria with lower risk of antibiotic resistance compared to conventional antibiotics. This proof of concept protocol demonstrates the potential for applying rational design of BAPCs as a delivery vector. Certain limitations, including off-target toxicity and redundant targeting are issues to be cognizant of and controlled. Conclusion: This novel therapy can be considered as a preliminary step in overcoming antibiotic resistance. With appropriate modifications to the functional ligands attached to the BAPCs, the proposed drug mechanism could be applied to treat other bacterial strains.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.383
Teacher spread0.333 · 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 designNot applicable
Domainnot available
GenreProtocol

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".

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Citations0
Published2020
Admission routes3
Has abstractyes

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