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Cited by 189

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Mini-review: Antimicrobial peptides and enzymes as promising candidates to functionalize biomaterial surfaces

Alves, Diana, Olívia Pereira, Maria·2014·Biofouling

Abstract

Biomaterial-associated infections remain a serious concern in modern healthcare. The development of materials that can resist or prevent bacterial attachment constitutes a promising approach to dealing with this problem. Antimicrobial peptides (AMPs) and enzymes have been recognized as promising candidates for the new generation of antimicrobial surfaces. AMPs have been the focus of great interest in recent years owing to a low propensity for developing bacterial resistance, broad-spectrum activity, high efficacy at very low concentrations, target specificity, and synergistic action with classical antibiotics. Biofilm-dispersing enzymes have been shown to inhibit biofilm formation, detach established biofilm, and increase biofilm susceptibility to other antimicrobials. This review critically examines the potential of these protein-like compounds for developing antibacterial coatings by reporting their immobilization into different substrata using different immobilization strategies.

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Metadata

Authors
Alves, D., & Olívia Pereira, M.
Venue
Biofouling
Published
2014-03-25
Volume
30
Issue
4
Pages
483-499
ISSN
  • 0892-7014 (ISSN-L)
  • 1029-2454
OpenAlex
W2000598102
Cited by
189
References
6
FWCI
2.55
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Mini-review: Antimicrobial peptides and enzymes as promising candidates to functionalize biomaterial surfaces. https://app.cochat.ai/papers/10.1080/08927014.2014.889120
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Alves, D., & Olívia Pereira, M. (2014). Mini-review: Antimicrobial peptides and enzymes as promising candidates to functionalize biomaterial surfaces. Biofouling. https://doi.org/10.1080/08927014.2014.889120

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.