Evidence map›Paper›PMID 31413942›Full record

ReviewTissue engineering and regenerative medicine2019

Preparation and Characterization of Human Adipose Tissue-Derived Extracellular Matrix, Growth Factors, and Stem Cells: A Concise Review.

So Young Chun, Jeong Ok Lim, Eun Hye Lee, Man-Hoon Han, Yun-Sok Ha, Jun Nyung Lee, Bum Soo Kim, Min Jeong Park, MyungGu Yeo, Bongsu Jung and 1 more

Open access · bronzeAbstract readReview
In one paragraph

Review in Tissue engineering and regenerative medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 1 pooled it
11.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.

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2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

So Young Chun *1BioMedical Research Institute, Joint Institute for Regenerative Medicine, Kyungpook National University Hospital, Daegu, 41940 Republic of Korea.
Jeong Ok Lim *1BioMedical Research Institute, Joint Institute for Regenerative Medicine, Kyungpook National University Hospital, Daegu, 41940 Republic of Korea.
Eun Hye Lee2Department of Pathology, School of Medicine, Kyungpook National University, Daegu, 41944 Republic of Korea.ORCID 0000-0001-5507-2632
Man-Hoon Han2Department of Pathology, School of Medicine, Kyungpook National University, Daegu, 41944 Republic of Korea.
Yun-Sok Ha3Department of Urology, School of Medicine, Kyungpook National University, Daegu, 41944 Republic of Korea.
Jun Nyung Lee3Department of Urology, School of Medicine, Kyungpook National University, Daegu, 41944 Republic of Korea.
Bum Soo Kim3Department of Urology, School of Medicine, Kyungpook National University, Daegu, 41944 Republic of Korea.
Min Jeong Park4Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Cheombok-ro 80, Dong-gu, Daegu, 41061 Republic of Korea.
MyungGu Yeo4Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Cheombok-ro 80, Dong-gu, Daegu, 41061 Republic of Korea.
Bongsu Jung *4Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Cheombok-ro 80, Dong-gu, Daegu, 41061 Republic of Korea.
Tae Gyun Kwon *3Department of Urology, School of Medicine, Kyungpook National University, Daegu, 41944 Republic of Korea.
Kyungpook National University · KRDaegu-Gyeongbuk Medical Innovation Foundation · KRKyungpook National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Human adipose tissue is routinely discarded as medical waste. However, this tissue may have valuable clinical applications since methods have been devised to effectively isolate adipose-derived extracellular matrix (ECM), growth factors (GFs), and stem cells. In this review, we analyze the literature that devised these methods and then suggest an optimal method based on their characterization results. Methods: Methods that we analyze in this article include: extraction of adipose tissue, decellularization, confirmation of decellularization, identification of residual active ingredients (ECM, GFs, and cells), removal of immunogens, and comparing structural/physiological/biochemical characteristics of active ingredients. Results: Human adipose ECMs are composed of collagen type I-VII, laminin, fibronectin, elastin, and glycosaminoglycan (GAG). GFs immobilized in GAG include basic fibroblast growth factor (bFGF), transforming growth factor beta 1(TGF-b1), insulin like growth factor 1 (IGF-1), vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), BMP4 (bone morphogenetic protein 4), nerve growth factor (NGF), hepatocyte growth factor (HGF), and epithermal growth factor (EGF). Stem cells in the stromal-vascular fraction display mesenchymal markers, self-renewal gene expression, and multi-differentiation potential. Conclusion: Depending on the preparation method, the volume, biological activity, and physical properties of ECM, GFs, and adipose tissue-derived cells can vary. Thus, the optimal preparation method is dependent on the intended application of the adipose tissue-derived products.

Indexed as

AdipocytesAdipose TissueCell DifferentiationExtracellular MatrixHumansIntercellular Signaling Peptides and ProteinsStem CellsIntercellular Signaling Peptides and ProteinsAdipose-derived stem cellExtracellular matrixGrowth factorsHuman adipose tissueOptimum method

Identifiers

PMID31413942
PMCPMC6675808
OpenAlexW2956109413

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.