Browsing by Author "Akhan, Ece"

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    Nanoparticle labeling: a new era in vivo tracing the cells in liver studies
    (Elsevier, 2012-04) Akhan, Ece; Aydın, Muammer Merve; İbrahimova, Vusala; Buğdaycı, Kadir Emre; Tuncel, Dönüş; Akçalı, Kamil Can
    Tracking molecules in vivo has always been challenging and there is increasing demand for tools that allow researchers to trace especially during cell-based therapies. Conjugated polymer based water-dispersible nanoparticles (NP) represent a new class of probes for cell imaging and tracking because they offer high brightness, improved photostability, high fluorescent quantum yield and non-cytotoxicity comparing to conventional dyes. We aimed to develop a novel approach by using fluorescein emitting NP to label Mesenchymal Stem Cells (MSCs) and HUH7 cell lines and assess their fate in vivo in liver regeneration, liver fibrosis and xenografts.
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    Role of FLT3 and its potential interaction partner, Hsp27 in hepatocellular carcinoma
    (Elsevier, 2012-04) Aydın, Muammer Merve; Bayın, N. S.; Akhan, Ece; Can, A.; Akçalı, Kamil Can
    Regeneration ability of liver is known to be associated with the activation of resident oval cells. Previously our group showed that oval cell marker, FLT3 changes its cellular localization in response to partial hepatectomy and suggested a role in liver regeneration. In addition, FLT3 was shown to play an important role in cellular proliferation and activation of PI3K and Ras. Hsp27 on the other hand, is a small chaperone that has roles in the inhibition of apoptosis and overexpressed in a wide range of cancers increasing their metastatic potential. Due to the role of FLT3 in liver regeneration and cellular proliferation, we aimed to analyze the effect of FLT3 in hepatocellular carcinoma (HCC) both in vitro and in vivo and its interaction with Hsp27.
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    Wavelet merged multi-resolution super-pixels and their applications on fluorescent MSC images
    (IEEE, 2015) Yorulmaz, Onur; Oğuz, Oğuzhan; Akhan, Ece; Tuncel, Dönüş; Atalay, R. Ç.; Çetin, A. Enis
    A new multi-resolution super-pixel based algorithm is proposed to track cell size, count and motion in Mesenchymal Stem Cells (MSCs) images. Multi-resolution super-pixels are obtained by placing varying density seeds on the image. The density of the seeds are determined according to the local high frequency components of the MSCs image. In this way a multi-resolution super-pixels decomposition of the image is obtained. A second contribution of the paper is novel decision rule for merging similar neighboring super-pixels. An algorithm based on well known wavelet decomposition is developed and applied to the histograms of neighboring super pixels to exploit similarity. The proposed algorithm is experimentally shown to be successful in segmenting and tracking cells in MSCs images.