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João L. Vilaça

Researcher at Ipca Laboratories Ltd.

Publications -  151
Citations -  1435

João L. Vilaça is an academic researcher from Ipca Laboratories Ltd.. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 17, co-authored 111 publications receiving 1043 citations. Previous affiliations of João L. Vilaça include Polytechnic Institute of Cávado and Ave & University of Minho.

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Fast automatic myocardial segmentation in 4D cine CMR datasets

TL;DR: The recent B-spline Explicit Active Surfaces framework is adapted to the properties of CMR images by integrating dedicated energy terms and the coupled BEAS formalism is extended towards its application in 3D MR data by adapting it to a cylindrical space suited to deal with the topology of the image data.
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Neuron-specific proteotoxicity of mutant ataxin-3 in C. elegans: rescue by the DAF-16 and HSF-1 pathways

TL;DR: A new Caenorhabditis elegans model of Machado-Joseph disease pathogenesis is described and it is revealed that the sequences flanking the polyQ-stretch in ATXN3 have a dominant influence on cell-intrinsic neuronal factors that modulatepolyQ-mediated pathogenesis.
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Targeting lactate transport suppresses in vivo breast tumour growth.

TL;DR: MCT knockdown led to a decrease in in vitro tumour cell aggressiveness, with decreased lactate transport, cell proliferation, migration and invasion and, importantly, to an inhibition of in vivo tumour formation and growth.
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Accuracy Comparison of Implant Impression Techniques: A Systematic Review.

TL;DR: A systematic review of peer-reviewed literature found the most accurate impression technique and factors affecting the impression accuracy was achieved with two configurations: the optical intraoral system with powder and the open technique with splinted squared transfer copings, using polyether as impression material.
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Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review.

TL;DR: A systematic review of the different methodologies developed for kidney segmentation found three approaches based on distinct image processing classes that can be used to accurately segment the kidney in images of different imaging modalities.