Synthesis and antibacterial properties of unmodified polydopamine coatings to prevent infections
Bibliographic record
Abstract
Health-care-associated infections (HAIs) can occur if a contaminated product bypasses current tests and prophylactic measures. These contaminations may be missed due to low bacterial loads or the presence of adhered biofilms. Antibacterial coatings applied inside blood storage bags or onto medical devices are promising to further reduce the residual risk of HAIs. The aim of this study was to optimize the antibacterial efficacy of a polymer — polydopamine — as a potential material for the prevention of transfusion-transmitted bacterial infections. When varying the concentration of dopamine monomers (1-3 mg/mL), the sample position (horizontal vs vertical), the stirring speed (0–90 RPM) and the reaction time (0.5 – 24 h), the morphology and wettability of the coatings were modified as determined by UV–visible (absorbance 0.013 – 0.562 at 320 nm), wettability (contact angle 35 – 61 °C) and atomic force microscopy measurements (total roughness 6 – 140 nm). The resulting cytotoxic (< 6%) and antibacterial behaviors (< 90 – 99% bacterial reduction) of the coatings were determined using ISO-10993–5 and ISO 22196 standardization. Coatings with good thickness and roughness had optimal antibacterial effects against Staphylococcus aureus (1.6 ± 0.4 log reduction), although minimal reduction was measured against Escherichia coli (0.05 log reduction). The antibacterial efficacy of polydopamine appears to be linked to its thickness and roughness, two parameters that may affect the surface wettability and, in turn, bacterial adhesion. Based on these results, polydopamine could be employed to help limit HAIs, although its antibacterial properties need to be further improved depending on the nature of bacteria and the requirements of the applications.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".