Polymer brush-based approaches for the development of infection-resistant surfaces
Bibliographic record
Abstract
In this highlight, we discuss the current strategies for developing infection-resistant biomaterials by making them non-fouling, bactericidal or both. We focus on approaches that have used polymer brush systems by providing examples of hydrophilic non-fouling polymer brushes, those that incorporated bactericidal agents (antibiotics, antimicrobial peptides and proteins) and synthetic polyelectrolyte polymer brushes. We discuss the most important research reported in recent years and deliberate their merits, future potential and further developments required. Initially we give a brief account on the use of anti-adhesive hydrophilic polymer brushes as bacteria-resistant surfaces and their potential utility in short term applications. The importance of the chemistry and physical properties of the brushes is highlighted along with the need for the development of bactericidal coatings. Further, recent developments involving bactericide-releasing and contact killing coatings are discussed. Approaches based on antimicrobial peptide conjugated polymer brushes, those incorporating enzymes (e.g. lysozyme), viruses and chemical functionalities (polyelectrolytes) that can kill bacteria are highlighted. As an important criterion for the in vivo application of infection-resistant coatings, the biocompatibility of the modified surfaces is briefly discussed in each section. The covalent attachment, availability of multitude of functionalities for further modification, ability to alter the physical structure of the coating, biocompatibility, potential application to various biomedical surfaces and the robust mechanical properties of polymer brush systems make them ideal for further development as a novel surface coating to address biomaterial-associated infections.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".