Evidence map›Paper›PMID 37323499›Full record

ArticleInternational journal of bioprinting2023

3D-bioprinted human lipoaspirate-derived cell-laden skin constructs for healing of full-thickness skin defects.

Dequan Zhang, Qiang Fu, Huijuan Fu, Jinshi Zeng, Litao Jia, Minliang Chen

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of bioprinting, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

10 citing papers in PubMed, 24 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Dequan ZhangSenior Department of Burns and Plastic Surgery, the Fourth Medical Center of PLA General Hospital, Beijing, 100048, China.
Qiang FuSenior Department of Burns and Plastic Surgery, the Fourth Medical Center of PLA General Hospital, Beijing, 100048, China.
Huijuan FuSenior Department of Burns and Plastic Surgery, the Fourth Medical Center of PLA General Hospital, Beijing, 100048, China.
Jinshi ZengResearch Center of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100144, China.
Litao JiaResearch Center of Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100144, China.
Minliang ChenSenior Department of Burns and Plastic Surgery, the Fourth Medical Center of PLA General Hospital, Beijing, 100048, China.
Chinese PLA General Hospital · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

29Three-dimensional (3D)-printed bioactive scaffolds that can be produced rapidly could offer an individualized approach for treating full-thickness skin defects. Decellularized extracellular matrix (dECM) and mesenchymal stem cells have been proven to support wound healing. Adipose tissues obtained by liposuction are rich in adipose-derived dECM (adECM) and adipose-derived stem cells (ADSCs) and thus represent a natural source of bioactive materials for 3D bioprinting. Herein, ADSC-laden 3D-printed bioactive scaffolds consisting of gelatin methacryloyl (GelMA), hyaluronic acid methacryloyl (HAMA), and adECM were fabricated with dual properties of photocrosslinking

Indexed as

3D bioprintingAdipose-derived decellularized extracellular matrixAdipose-derived stem cellsFull-thickness skin defectLipoaspirateWound healing

Identifiers

PMID37323499
PMCPMC10261198
OpenAlexW4360865701

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.