Evidence map›Paper›PMID 37015903›Full record

SynthesisInternational wound journal2023

Effect of mesenchymal stromal cells-derived extracellular vesicles as a treatment to heal diabetic wounds: A meta-analysis.

Xiaocen Chang, Jia Li

RetractedAbstract readMeta-AnalysisRetracted Publication
In one paragraph

Synthesis in International wound journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Xiaocen ChangDepartment of Endocrinology and Metabolism, the Fourth Affiliated Hospital, China Medical University, Shenyang, Liaoning, 110032, China.
Jia LiDepartment of Endocrinology and Metabolism, the Fourth Affiliated Hospital, China Medical University, Shenyang, Liaoning, 110032, China.ORCID https://orcid.org/0009-0001-4032-4179

Funding

Doctoral Start-up Foundation of Liaoning Province 20180540008
6 · The paper itself

Abstract

A meta-analysis study to assess the influence of mesenchymal stromal cells-derived extracellular vesicles (MSC-EVs) as a treatment to heal the diabetic wound (DW). A comprehensive literature examination till February 2023 was implemented and 2975 linked studies were appraised. The picked studies contained 381 animals with diabetes mellitus in the picked studies' baseline, 217 of them were using MSC-EVs, and 173 were using control. Odds ratio in addition to 95% confidence intervals (CIs) were used to calculate the consequence of MSC-EVs as a therapy to heal DWs by the dichotomous and continuous styles and a fixed or random model. MSCs-EVs had a significantly higher rate of wound closure of DWs (Mean deviation [MD], 22.20; 95% CI, 19.16-25.24, P < .001), lower width of the scar (MD, -2.57; 95% CI, -3.35 to -1.79, P < .001), higher collagen deposition (MD, 30.82; 95% CI, 20.77-40.86, P < .001), and a higher rate of re-epithelialisation (MD, 34.36; 95% CI, 20.13-48.58, P < .001) compared with the control. MSCs-EVs had a significantly higher rate of wound closure of DWs, lower width of the scar, higher collagen deposition, and higher rate of re- epithelialisation compared with the control. Although precautions should be taken when commerce with the consequences because all of the picked studies for this meta-analysis was with low sample sizes.

Indexed as

Diabetes MellitusExtracellular VesiclesMesenchymal Stem CellsAnimalsCicatrixWound Healingdiabetes mellitusdiabetic woundsmesenchymal stromal cells-derived extracellular vesicleswound closure

Identifiers

PMID37015903
PMCPMC10410336

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.