Evidence map›Paper›PMID 33463346›Full record

ArticleACS biomaterials science & engineering2020

Mixed Composition Microribbon Hydrogels Induce Rapid and Synergistic Cartilage Regeneration by Mesenchymal Stem Cells in 3D

Courtney Gegg, Xinming Tong, Fan Yang

Abstract read
In one paragraph

Article in ACS biomaterials science & engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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  5. 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

3 authors.

Courtney GeggDepartment of Bioengineering, Stanford University Schools of Engineering and Medicine, Stanford, California 94305, United States.
Xinming TongDepartment of Orthopedic Surgery, Stanford University School of Medicine, Stanford, California 94305, United States.
Fan YangDepartments of Bioengineering and Orthopedic Surgery, Stanford University Schools of Engineering and Medicine, 300 Pasteur Drive, Edwards R105, Stanford, California 94305, United States.ORCID 0000-0002-0418-0403

Funding

Microribbon scaffold-mediated Immunomodulation for Cranial Bone RepairR01DE024772 · NIDCR · STANFORD UNIVERSITY · PI YANG, FAN · 2015 to 2025
$4.6M
Sliding hydrogels for accelerating cartilage regenerationR01AR074502 · NIAMS · STANFORD UNIVERSITY · PI YANG, FAN · 2019 to 2023
$2.2M
NIAMS NIH HHS R01 AR074502NIDCR NIH HHS R01 DE024772
6 · The paper itself

Abstract

Hydrogels are widely used matrices for mesenchymal stem cell (MSC)-based cartilage regeneration but often result in slow cartilage deposition with inferior mechanical strength. We recently reported a gelatin-based microribbon (μRB) scaffold, which contains macroporosity and substantially enhances the speed of cartilage formation by MSCs in 3D. However, our previous method cannot be used to fabricate different polymers into μRBs, and the effects of varying μRB compositions on MSC cartilage regeneration in 3D remain unknown. Here, we report a method that allows fabricating different polymers [gelatin, chondroitin sulfate, hyaluronic acid, and polyethylene glycol (PEG)] into μRB structures, which can be mixed in any ratio and cross-linked into 3D scaffolds in a modular manner. Mixing glycosaminoglycan μRBs with gelatin or PEG μRBs induced great synergy, resulting in fast cartilage deposition. After only 3 weeks of culture, leading mixed μRB composition reached high compressive strength on par with native cartilage. Such synergy can be recapitulated

Indexed as

HydrogelsMesenchymal Stem CellsCartilageParacrine CommunicationRegenerationHydrogelscartilagehydrogelsscaffoldsstem cellstissue engineering

Identifiers

PMID33463346
PMCPMC10154175

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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.