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Injectable hydrogel scaffold from decellularized human lipoaspirate

Open access
Young, D. Adam, Ibrahim, Dina O., Hu, Diane, Christman, Karen L.·2011·Acta Biomaterialia

Abstract

Soft tissue fillers are rapidly gaining popularity for aesthetic improvements or repair of adipose tissue deficits. Several injectable biopolymers have been investigated for this purpose but often face rapid resorption or limited adipogenesis, and do not mimic the native adipose extracellular matrix (ECM). We have generated an injectable adipose matrix scaffold by efficiently removing both the cellular and lipid contents of human lipoaspirate. The decellularized material retained a complex composition of peptides and glycosaminoglycans found in native adipose ECM. This matrix can be further processed by solubilizing the extracted ECM to generate a thermally-responsive hydrogel that self-assembles upon subcutaneous injection. This hydrogel also supports the growth and survival of patient matched adipose - derived stem cells in vitro. The development of an injectable hydrogel from human lipoaspirate represents a minimally-invasive option for adipose tissue engineering in terms of both the collection of source material and delivery of the scaffold.

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Crossref · 10.1016/j.actbio.2010.09.035

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Metadata

Authors
Young, D. A., Ibrahim, D. O., Hu, D., & Christman, K. L.
Venue
Acta Biomaterialia
Publisher
Elsevier BV
Published
2011-03
Volume
7
Issue
3
Pages
1040-1049
ISSN
1742-7061 (ISSN-L)
OpenAlex
W1997671734
Cited by
213
References
3
FWCI
3.08
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Injectable hydrogel scaffold from decellularized human lipoaspirate. https://app.cochat.ai/papers/10.1016/j.actbio.2010.09.035
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Young, D. A., Ibrahim, D. O., Hu, D., & Christman, K. L. (2011). Injectable hydrogel scaffold from decellularized human lipoaspirate. Acta Biomaterialia. https://doi.org/10.1016/j.actbio.2010.09.035

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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.