Synthesis and antibacterial properties of unmodified polydopamine coatings to prevent infections
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 it