ReviewJournal of orthopaedic translation2019
Optimization strategies for ACI: A step-chronicle review.
Review in Journal of orthopaedic translation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Bioenergetic-active exosomes for cartilage regeneration and homeostasis maintenance.Science advances · 2024Article
- Enhanced articular cartilage regeneration using costal chondrocyte-derived scaffold-free tissue engineered constructs with ascorbic acid treatment.Journal of orthopaedic translation · 2024Article
- Hypoxia Preconditioned Serum (HPS) Promotes Proliferation and Chondrogenic Phenotype of Chondrocytes In Vitro.International journal of molecular sciences · 2023Article
- The role of TGF-beta3 in cartilage development and osteoarthritis.Bone research · 2023Review
- An immunomodulatory polypeptide hydrogel for osteochondral defect repair.Bioactive materials · 2023Article
- Studies of Articular Cartilage Repair from 2009 to 2018: A Bibliometric Analysis of Articles.Orthopaedic surgery · 2021Review
- Sustained benefit of autologous matrix-induced chondrogenesis for hip cartilage repair in a recreational athletic population.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2020Article
- Tissue Specific Differentiation of Human Chondrocytes Depends on Cell Microenvironment and Serum Selection.Cells · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bearing compression from adjacent joints, the articular cartilage is cumulatively pressured in daily life, thus making it prone to injuries; however, once damaged, the self-healing capacity of articular cartilage is limited owing to its low metabolic property. Autologous chondrocyte implantation, a three-step repairing technique for articular lesions, has received satisfactory short-term clinical outcomes, whereas its long-term effect remains controversial. Currently, improved stem-cell therapies and novel biomaterials have shed new lights on autologous chondrocyte implantation. We would, therefore, synthesize these optimization strategies in order of their presences in the three-step protocol, seeking to find and amplify synergic effects between these strategies. THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE: Autologous chondrocytes implantation serves as an alternative for the treatment of articular cartilage lesions to avoid potentially detrimental effects of applying microfracture. The optimized ACI should improve the cost-effectiveness of repairing articular cartilage while circumventing latent complications like osteophyte. This article synthesized optimization strategies for ACI and provided appropriate applying approaches to maximize their synergic effects. It will be a pioneering trial for combinedly using stem cells and nanotechnology to regenerate cartilage.
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Registered trials
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.