Evidence map›Paper›PMID 37744322›Full record

ArticleJournal of tissue engineering

Reconstructing vascular networks promotes the repair of skeletal muscle following volumetric muscle loss by pre-vascularized tissue constructs.

Chih-Long Chen, Shih-Yen Wei, Wei-Lin Chen, Ting-Lun Hsu, Ying-Chieh Chen

Abstract read
In one paragraph

Article in Journal of tissue engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

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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

5 authors.

Chih-Long ChenDepartment of Dentistry, Shin Kong Wu Ho-Su Memorial Hospital, Taipei.
Shih-Yen WeiDepartment of Materials Science and Engineering, National Tsing-Hua University, Hsinchu.
Wei-Lin ChenDepartment of Materials Science and Engineering, National Tsing-Hua University, Hsinchu.
Ting-Lun HsuDepartment of Materials Science and Engineering, National Tsing-Hua University, Hsinchu.
Ying-Chieh ChenDepartment of Materials Science and Engineering, National Tsing-Hua University, Hsinchu.ORCID https://orcid.org/0000-0001-9754-5362

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current treatment for complex and large-scale volumetric muscle loss (VML) injuries remains a limited success and have substantial disadvantages, due to the irreversible loss of muscle mass, slow muscle regeneration, and rapid formation of non-functional fibrosis scars. These VML injuries are accompanied by denervation and the destruction of native vasculature which increases difficulties in the functional restoration of muscle. Here, reconstruction of the vascular network at the injury site was offered as a possible solution for improving the repair of muscle defects through the timely supply of nutrients and oxygen to surrounding cells. A hydrogel-based tissue construct containing various densities of the vascular network was successfully created in the subcutaneous space of mice by manipulating hydrogel properties, and then implanted into the VML injury site. One month after implantation, the mouse treated with the highly vascularized tissue had extensive muscle repair at the injury site and only spent a shorter time completing the inclined plane tests. These findings suggest that the reconstruction of the functional vascular network at the VML injury site accelerated muscle fiber repair through a timely supply of sufficient blood and avoided invasion by host fibroblasts.

Indexed as

cell-laden hydrogelmuscle repairpreformed vascular networksvascular tissue engineeringVolumetric muscle loss (VML)

Identifiers

PMID37744322
PMCPMC10517612

What OpenQuestion holds

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Registered trials

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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.