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Cited by 274

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

Bioinspired Antimicrobial and Biocompatible Bacterial Cellulose Membranes Obtained by Surface Functionalization with Aminoalkyl Groups

Fernandes, Susana C.M., Sadocco, Patrizia, Alonso-Varona, Ana, Palomares, Teodoro, Eceiza, Arantxa, Silvestre, Armando J.D., Mondragon, Iñaki, Freire, Carmen S.R.·2013·ACS Applied Materials & Interfaces

Abstract

There has been a great deal of interest in the use of nanostructured bacterial cellulose membranes for biomedical applications, including tissue implants, wound healing, and drug delivery. However, as bacterial cellulose does not intrinsically present antimicrobial properties, in the present study, antimicrobial bacterial cellulose membranes were obtained by chemical grafting of aminoalkyl groups onto the surface of its nanofibrillar network. This approach intends to mimic intrinsic antimicrobial properties of chitosan. Interestingly, these novel grafted bacterial cellulose membranes (BC-NH2) are simultaneously lethal against S. aureus and E. coli and nontoxic to human adipose-derived mesenchymal stem cells (ADSCs) and thus may be useful for biomedical applications. In addition to these biological properties, the bioactive nanostructured BC-NH2 membranes also present improved mechanical and thermal properties.

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Registry

Crossref · 10.1021/am400338n

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Metadata

Authors
Fernandes, S. C. M., Sadocco, P., Alonso-Varona, A., Palomares, T., Eceiza, A., Silvestre, A. J. D., Mondragon, I., & Freire, C. S. R.
Venue
ACS Applied Materials & Interfaces
Published
2013-04-11
Volume
5
Issue
8
Pages
3290-3297
ISSN
  • 1944-8244 (ISSN-L)
  • 1944-8252
OpenAlex
W1975809077
Cited by
274
References
54
FWCI
6.23
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.