A new tool to attack biofilms: driving magnetic iron-oxide nanoparticles to disrupt the matrix
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".