Peptide-Functionalized Nanopillared Surfaces with Tunable Antimicrobial and Immunomodulatory Properties
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Stimuli-responsive nanopatterned surfaces developed by physical and chemical modification with polymers, nanoparticles, and biomolecules have found various applications in biotechnology and biomedicines. In this study, bacterial capture and killing abilities of cicada wing-inspired, highly hydrophilic, nanopillared surfaces composed of poly(methacrylic acid)- co -poly(vinyl alcohol) are combined with cationic amphipathic peptides to develop antimicrobial and immunomodulatory materials with tunable properties. The peptide-modified surfaces prepared by layer-by-layer deposition were analyzed for the amount of peptide deposited and demonstrated significant changes in surface hydrophilicity and height of nanopillars upon biomolecule immobilization. The deposition of an antimicrobial peptide on nanopillared surfaces reduced the antimicrobial properties of the bare surfaces and the free peptide, while the immobilized peptide activated Toll-like receptors of macrophages, thereby enhancing the immunomodulatory properties of the materials. The gradual degradation of peptides on the modified surfaces in biological milieu slowly dampened immunomodulatory properties and restored the antimicrobial efficacies of the bare surfaces as a function of time, suggesting the development of tunable and regenerative materials with potential applications in wound healing.
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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".