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Search: WFRF:(Bloch M) > Engineering and Technology

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1.
  • Bloch, K, et al. (author)
  • Functional activity of insulinoma cells (INS-1E) and pancreatic islets cultured in agarose cryogel sponges
  • 2005
  • In: Journal of Biomedical Materials Research. Part A. - : Wiley. - 1552-4965 .- 1549-3296. ; 75A:4, s. 802-809
  • Journal article (peer-reviewed)abstract
    • Here, we describe the preparation, structure, and properties of cryogel sponges, which represent a new type of macroporous biomaterial for tissue engineering. Cryogels were produced through freeze-thawing techniques, either from agarose alone or from agarose with grafted gelatin. The aim of this study was to evaluate agarose cryogel sponges as scaffolds for Culturing both isolated pancreatic islets and insulinoma cells (INS-IE). In order to evaluate the effect of cell entrapment in artificial scaffolds, cell function reflected by insulin secretion and content was studied in cells cultivated for a 2-week period either in Culture plastic plates or in cryogel sponge disks. Our results show that tumor-derived INS-1E cells grown either on plastic or on cryogels do not differ in their proliferation, morphology, insulin release, and intracellular insulin content. However, isolated pancreatic islets cultivated on cryogels sponge show 15-fold higher basal insulin secretion at 3.0 mM glucose than islets cultivated on plastic plates and fail to respond to stimulation with 16.7 mM glucose. In addition, these islets have about 2-fold lower insulin content compared to those grown in plastic plates. It is possible that the cell dysfunction noted in these in vitro experiments is due to the effect of the limited oxygen supply to the islets cultivated in cryogel sponge. Further in vivo Studies are needed to clarify the nature of such an observation since according to previous reports, agarose and gelatin induce new vessel formation supporting enhanced oxygen supply. (c) 2005 Wiley Periodicals, Inc.
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2.
  • Commowick, Olivier, et al. (author)
  • Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
  • 2018
  • In: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 8
  • Journal article (peer-reviewed)abstract
    • We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning,.), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
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