ArticleTissue engineering. Part C, Methods2016
Imaging of Hydrogel Microsphere Structure and Foreign Body Response Based on Endogenous X-Ray Phase Contrast.
Article in Tissue engineering. Part C, Methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- X-ray CT in Phase Contrast Enhancement Geometry of Alginate Microbeads in a Whole-Animal Model.Annals of biomedical engineering · 2020Article
- Dual Crosslinking of Alginate Outer Layer Increases Stability of Encapsulation System.Frontiers in chemistry · 2020Article
- Recent progress in the use and tracking of transplanted islets as a personalized treatment for type 1 diabetes.Expert review of precision medicine and drug development · 2017Article
- The emerging field of pancreatic tissue engineering: A systematic review and evidence map of scaffold materials and scaffolding techniques for insulin-secreting cells.Journal of tissue engineeringReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Transplantation of functional islets encapsulated in stable biomaterials has the potential to cure Type I diabetes. However, the success of these materials requires the ability to quantitatively evaluate their stability. Imaging techniques that enable monitoring of biomaterial performance are critical to further development in the field. X-ray phase-contrast (XPC) imaging is an emerging class of X-ray techniques that have shown significant promise for imaging biomaterial and soft tissue structures. In this study, XPC imaging techniques are shown to enable three dimensional (3D) imaging and evaluation of islet volume, alginate hydrogel structure, and local soft tissue features ex vivo. Rat islets were encapsulated in sterile ultrapurified alginate systems produced using a high-throughput microfluidic system. The encapsulated islets were implanted in omentum pouches created in a rodent model of type 1 diabetes. Microbeads were imaged with XPC imaging before implantation and as whole tissue samples after explantation from the animals. XPC microcomputed tomography (μCT) was performed with systems using tube-based and synchrotron X-ray sources. Islets could be identified within alginate beads and the islet volume was quantified in the synchrotron-based μCT volumes. Omental adipose tissue could be distinguished from inflammatory regions resulting from implanted beads in harvested samples with both XPC imaging techniques. Individual beads and the local encapsulation response were observed and quantified using quantitative measurements, which showed good agreement with histology. The 3D structure of the microbeads could be characterized with XPC imaging and failed beads could also be identified. These results point to the substantial potential of XPC imaging as a tool for imaging biomaterials in small animal models and deliver a critical step toward in vivo imaging.
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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.