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Record W2924307610 · doi:10.1039/c8nr09802f

A new tool to attack biofilms: driving magnetic iron-oxide nanoparticles to disrupt the matrix

2019· article· en· W2924307610 on OpenAlex
Jie Li, Rachel Nickel, Jiandong Wu, Francis Lin, J. van Lierop, Song Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsNanoparticleBiofilmIron oxide nanoparticlesMatrix (chemical analysis)Magnetic nanoparticlesIron oxideMaterials scienceNanotechnologyChemical engineeringMetallurgyBacteriaEngineeringComposite materialGeology

Abstract

fetched live from OpenAlex

A main feature of biofilms is the self-produced extracellular polymeric substances (EPSs) that act as a protective shield, preventing biocide penetration. We use magnetic iron oxide nanoparticles (MNPs) in combination with magnetic fields to damage the biofilm matrix and cause detachment. A Methicillin-resistant Staphylococcus aureus (MRSA) biofilm strain is used to demonstrate the efficacy of the methodology with different sizes and concentrations of MNPs under AC and DC applied field conditions. We achieve up to a nearly 5 log10 reduction in biofilm bacteria after treatment with 30 mg mL-1 of 11 nm MNPs using a magnetic field. The MNPs cause significant mechanical disruption to the matrix and lead to biofilm dispersal. In addition, using magnetic hyperthermia further affects biofilm damage.

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.

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 score0.178
Threshold uncertainty score1.000

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)0.0000.001

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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