P
P. Van Oostveldt
Researcher at Ghent University
Publications - 41
Citations - 1154
P. Van Oostveldt is an academic researcher from Ghent University. The author has contributed to research in topics: Confocal & Epicotyl. The author has an hindex of 19, co-authored 41 publications receiving 1102 citations.
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Journal ArticleDOI
Entry of porcine reproductive and respiratory syndrome virus into porcine alveolar macrophages via receptor-mediated endocytosis.
TL;DR: Results indicate that PRRSV entry into AMphi involves attachment to a specific virus receptor(s) followed by a process of endocytosis, by which virions are taken into the cell within vesicles by a clathrin-dependent pathway.
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Presence and dynamic redistribution of type I inositol 1,4,5-trisphosphate receptors in human oocytes and embryos during in-vitro maturation, fertilization and early cleavage divisions.
TL;DR: Type I IP3R are likely to play a role during these stages of early development in the human and possibly represent the dynamic changes occurring in the cellular Ca2+ release systems through oocyte maturation, fertilization and early embryogenesis.
Journal ArticleDOI
Diffusion of Macromolecules in Dextran Methacrylate Solutions and Gels As Studied by Confocal Scanning Laser Microscopy
S.C. De Smedt,Tom K. L. Meyvis,Joseph Demeester,P. Van Oostveldt,J. C. G. Blonk,Wim E. Hennink +5 more
TL;DR: In this paper, the authors used confocal scanning laser microscopy (CSLM) and fluorescence recovery after photobleaching (FRAP) to study the mobility of large molecules in polymer systems.
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Telomere length versus hormonal and bone mineral status in healthy elderly men.
Sofie Bekaert,I Van Pottelbergh,T De Meyer,Hans Zmierczak,JM Kaufman,P. Van Oostveldt,Stefan Goemaere +6 more
TL;DR: The predictive value of telomere length for bone loss appears to be substantial, hence underscoring the role of telitere length as a biomarker of ageing phenotypes.
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High content image cytometry in the context of subnuclear organization
TL;DR: The presented approach allowed for systematic analysis of subnuclear features in large image data sets and accurate classification of phenotypes at the level of the single cell, and shows potential for high‐throughput applications, such as functional protein assays or drug compound screening.