Activating Thermoplastic Polyurethane Surfaces with Poly(ethylene glycol)-Based Recombinant Human α-Defensin 5 Monolayers for Antibiofilm Activity
Bibliographic record
Abstract
Addressing multidrug-resistant microbial infections linked to implantable biomedical devices is an urgent need. In recent years, there has been an active exploration of different surface coatings to prevent and combat drug-resistant microbes. In this research, we present a facile chemical modification of thermoplastic polyurethane (TPU) surfaces with poly(ethylene glycol)-based recombinant human α-defensin 5 (HD5) protein with antimicrobial activity. TPU is one of the most relevant materials used for medical devices with good mechanical properties but also good chemical resistance, which makes it difficult to modify. The chemical modification of TPU surfaces is achieved via a three-step procedure based on (i) TPU activation using hexamethylene diisocyanate (HDI); (ii) interfacial reaction with poly(ethylene glycol) (PEG) derivatives; and finally, (iii) a facile click reaction between the PEG-maleimide terminated assembled monolayers on the TPU and the cysteine (-thiol) termination of the recently designed recombinant human α-defensin 5 (HD5) protein. The obtained PEG based HD5 assembled monolayers on TPU were characterized using a surface science multitechnique approach including scanning electron microscopy, atomic force microscopy, contact angle, and X-ray photoelectron spectroscopy. The modified TPU surfaces with the HD5 protein derivative exhibit broad-spectrum antibacterial properties reducing biofilm formation against Pseudomonas aeruginosa (Gram-negative), methicillin-resistant Staphylococcus aureus (MRSA) (Gram-positive) and methicillin-resistant Staphylococcus epidermidis (MRSE) (Gram-positive). These findings underscore the substantial potential of protein-modified TPU surfaces for applications in combating bacterial infections associated with implantable materials and devices.
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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) | 0.001 | 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".