Evidence map›Paper›PMID 37711133›Full record

SynthesisCurrent stem cell research & therapy2024

Comparative Efficacy of Endogenous Stem Cells Recruiting Hydrogels and Stem Cell-loaded Hydrogels in Knee Cartilage Regeneration: A Meta- analysis.

Wanyun Feng, Chaohua Zhu, Ruoxiang Miao, Danni Li, Xi Xiong, Ruyu Wang, Guobin Liu, Jun Ma

Abstract readMeta-AnalysisComparative Study
PubMed Publisher
In one paragraph

Synthesis in Current stem cell research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Wanyun FengHebei Medical University-National University of Ireland Galway Stem Cell Research Center, Hebei Medical University, Hebei Province, 050017, China.
Chaohua ZhuThe First Hospital of Hebei Medical University, Hebei Province, 050000, China.
Ruoxiang MiaoHebei Medical University-National University of Ireland Galway Stem Cell Research Center, Hebei Medical University, Hebei Province, 050017, China.
Danni LiHebei Medical University-National University of Ireland Galway Stem Cell Research Center, Hebei Medical University, Hebei Province, 050017, China.
Xi XiongHebei Medical University-National University of Ireland Galway Stem Cell Research Center, Hebei Medical University, Hebei Province, 050017, China.
Ruyu WangHebei Medical University-National University of Ireland Galway Stem Cell Research Center, Hebei Medical University, Hebei Province, 050017, China.
Guobin LiuThe First Hospital of Hebei Medical University, Hebei Province, 050000, China.
Jun MaHebei Medical University-National University of Ireland Galway Stem Cell Research Center, Hebei Medical University, Hebei Province, 050017, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCartilage defects remain a challenge in diseases such as osteoarthritis (OA) and fractures. Scientists have explored the use of hydrogels in conjunction with stem cell technology as a tissue engineering method to treat cartilage defects in joints. In recent years, research into hydrogels containing stem cell technology for cartilage repair has mainly focused on two categories: stem cell-loaded hydrogels and endogenous stem cell recruiting hydrogels. The latter, utilizing cell-free products, represents a novel concept with several advantages, including easier dose standardization, wider sources, and simpler storage. This meta-analysis aims to assess and compare the therapeutic effects of endogenous stem cell recruiting hydrogels and stem cell-loaded hydrogels in promoting articular cartilage regeneration in animal models, with the goal of exploring endogenous stem cell recruiting hydrogels as a promising replacement therapy for knee cartilage regeneration in preclinical animal studies.

methodsWe systematically searched PubMed, Web of Science, Cochrane Library, and Embase until January 2023 using key words related to stem cells, cartilage regeneration and hydrogel. A random-effects meta-analysis was performed to evaluate the therapeutic effect on newborn cartilage formation. Stratified analyses were also carried out by independently classifying trials according to similar characteristics. The level of evidence was determined using the GRADE method.

resultsTwenty-eight studies satisfied the inclusion criteria. Comprehensive analyses revealed that the use of endogenous stem cell recruiting hydrogels significantly promoted the formation of new cartilage in the knee joint, as evidenced by the histological score (3.77, 95% CI 2.40, 5.15; p < 0.0001) and the International Cartilage Repair Society (ICRS) macroscopic score (3.00, 95% CI 1.83, 4.18; p = 0.04), compared with the control group. The stem cell-loaded hydrogels also increased cartilage regeneration in the knee with the histological score (3.13, 95% CI 2.22, 4.04; p = 0.02) and the ICRS macroscopic score (2.49, 95% CI 1.16, 3.82; p = 0.03) in comparison to the control. Significant heterogeneity between studies was observed, and further stratified and sensitivity analyses identified the transplant site and modelling method as the sources of heterogeneity.

conclusionThe current study indicates that both endogenous stem cell recruiting hydrogels and stem cell loaded hydrogels can effectively promote knee joint cartilage regeneration in animal trials.

Indexed as

Cartilage, ArticularHydrogelsRegenerationStem CellsAnimalsHumansKnee JointStem Cell TransplantationTissue EngineeringHydrogelsAnimal trialcartilage regenerationheterogeneity.hydrogelmeta-analysisstem cell

Identifiers

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.