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Book
Geïntegreerd metabolisme.
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ISBN: 9789033492686 9789033492839 Year: 2013 Publisher: Leuven Acco

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Pathologische biochemie
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Publisher: Leuven Medica

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Regulation and specificity of the polycation-stimulated protein phosphatases
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ISSN: 07707703 ISBN: 9061862736 Year: 1988 Volume: 5 Publisher: Leuven University Press

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Dissertation
Epithelial ovarian cancer : molecular and clinical predictors for platinum resistance.
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Year: 2012 Publisher: Leuven KU Leuven. Faculteit Geneeskunde. Departement Oncologie

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Medische biochemie
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Year: 1997 Publisher: Leuven Medica

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Dissertation
The evaluation of new biomarkers in gynaecological tumours : The emerging role of Proteomics.
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ISBN: 9789090266817 Year: 2012 Publisher: Leuven KU Leuven. Faculteit Geneeskunde. Departement Oncologie

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Dissertation
Transcriptomic/proteomic approach to detect biomarkers in endometriosis.
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Year: 2011 Publisher: Leuven K.U.Leuven. Faculteit Geneeskunde

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Endometriosis is an estrogen dependent multi-factorial disease which affects around 10% of women of reproductive age. It is defined as the presence of endometrium-like tissue in sites outside the uterine cavity. Notwithstanding, the etiology and pathogenesis of endometriosis remain uncertain. Combinations of genetic, hormonal, environmental and immunological factors play a role in the pathogenesis of this disorder. Until today, no semi- or non-invasive test has been developed for the diagnosis of endometriosis. The gold standard for the diagnosis of pelvic disease is surgical assessment by laparoscopy. The most important goal of a non-invasive diagnostic test is to identify women with endometriosis who might benefit from surgical treatment for endometriosis-associated pain or subfertility.The overall aim of this thesis was firstly to investigate the pathogenesis of endometriosis using macroscopically normal peritoneum and eutopic endometrium from women with and without endometriosis and secondly, to discover new biomarkers in order to develop a semi- or non-invasive diagnostic test for endometriosis, using endometrium and plasma samples from women with and without endometriosis.We found increased IL-6 mRNA and reduced IL-12 mRNA expression in macroscopically normal peritoneum. This altered gene expression may concurrently contribute to the pathogenesis of endometriosis via enhanced inflammation and via a reduction of natural killer (NK) cytotoxicity. The reduced ferritin mRNA expression in macroscopically normal peritoneum from women with endometriosis suggests that iron overload may be limited to endometriosis lesions and not extend to normal peritoneum. This study indicates that the immunobiology of macroscopically normal peritoneum is relevant to understand the pathogenesis of endometriosis.Proteomic Surface Enhanced Laser Desorption/Ionisation (SELDI-TOF) mass spectrometry (MS) analysis of plasma samples allowed the diagnosis of endometriosis using 5 protein or peptide peaks with high sensitivity (minimal-mild=75%, moderate-severe=98%) and high specificity (minimal-mild=86%, moderate-severe=81%), based on the analysis of a training and test set using a randomization approach. The peak with the highest intensity (2.189Da) was decreased in women with moderate-severe endometriosis when compared to controls and was identified as fibrinogen beta chain peptide. In our combined endometrium microarray and SELDI-TOF MS analysis we showed a clear difference in gene expression in menstrual phase compared to early luteal phase in patients with and without endometriosis. In our endometrium proteomics part of the study we were able to classify minimal-mild versus control using 5 protein or peptide peaks with a sensitivity of 94% and specificity of 100% and when combining minimal-severe with sensitivity of 91% and specificity of 80%. In this dissertation we propose a semi- and non-invasive way to diagnose endometriosis with a high sensitivity and high specificity.

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Dissertation
Manifold learning for visualization, prioritization, and data fusion of Mass Spectrometry Imaging data
Authors: --- --- ---
Year: 2021 Publisher: Leuven KU Leuven. Faculty of Engineering Science

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Mass Spectrometry Imaging (MSI) is a powerful molecular imaging technology that can detect the spatial distribution of molecules in a tissue section. Because MSI does not require any a priori labeling, the technique has become very popular for the explorative comparison of metabolites, lipids, peptides and proteins between various tissue regions. Since it has been shown that tumor heterogeneity plays an important role in tumor biology, it has become clear that we need to elucidate the spatial distribution of molecules. MSI can therefore be of significant relevance in predicting cancer progression and treatment response, which often remains a challenge in today's clinical practice. A single measurement can however lead to complex and high dimensional data with file sizes in the gigabyte and even the terabyte range. As such manually exploring the data is becoming infeasible and support from computational methods is required.The focus of this work is therefore the development and application of computational methods to MSI data. Specifically we have concentrated on the topics related to non-linear dimensionality reduction, the prioritization of molecules measured per tissue region, and data fusion of MSI with the corresponding histology or microscopy image. For the non-linear dimensionality reduction, we make use of Uniform Manifold Approximation and Projection (UMAP) through which we achieve excellent visualizations of MSI data as demonstrated by the corresponding histology or microscopy images. We have conducted an extensive evaluation regarding the performance and results in comparison to other dimensionality reduction methods. To this end, we have used spatial autocorrelation and spectral similarity as a benchmark. In addition, we have empirically evaluated a number of different distance metrics, where we show that the choice of a particular distance metric might impact the visualization outcome. Building further on the obtained visualizations using UMAP, we proposed a bi-directional dimensionality reduction approach to prioritize the molecules driving these observations. This approach enables the prioritization of m/z-values in individual tissue samples but also across different tissue samples through the incorporation of both spatial and spectral information. The approach was demonstrated for tissue samples obtained from healthy mouse pancreas tissue. Finally, we introduce the correspondence-aware manifold learning paradigm for data fusion of molecular imaging data with the corresponding microscopy images. This enables us to bring the molecular information to a higher spatial resolution. As such these visualizations can play an important role in the digital pathology field for the quick assessment of a complete MSI dataset from a pathologist's perspective. We have shown that using this approach it becomes possible to identify a single infiltrating plasma cell amongst a group of phenotypically different (epithelial) cells. The identification of single aberrant cells is of crucial importance to evaluate a wide range of pathologies, in particular in cancer.

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Dissertation
Identification of PPP2R4 as a novel candidate tumor suppressor : evidence from PTPA gene trapped mice and human tumors
Authors: --- --- --- ---
Year: 2014 Publisher: Leuven KU Leuven. Faculty of Medicine

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Protein Phosphatase 2A (PP2A) complexes counteract diverse kinase-driven oncogenic pathways. Accumulating clinical evidence further underscores impaired PP2A function/activity in diverse cancers, sustaining its suspected tumor suppressor function. Nevertheless, whether loss of PP2A activity is sufficient for tumorigenesis in vivo has remained elusive. Here, we describe development of spontaneous malignancies in mice (haplo)deficient for Ppp2r4, encoding a PP2A chaperone (PTPA) essential for generation of active PP2A holoenzymes. PTPA-deficient tissues show reduced PP2A activity and methylation, selectively affecting specific PP2A holoenzymes. Complementary analyses of protein phosphorylation and gene expression revealed heterogeneous activation of diverse oncogenic signaling pathways in the tumors, underscoring that decreased PP2A activity affects multiple targets. Importantly, cancer database surveys revealed heterozygous PPP2R4 deletion at strikingly high frequency in several human cancer types. Furthermore, cancer-derived PPP2R4 mutants showing impaired PP2A-C binding in cellulo or impaired PTPA activity in vitro were unable to rescue transformation of PTPA-depleted human HEK-TER cells. Our data provide the first compelling in vivo evidence that impaired PP2A activity is sufficient to promote tumor development and establish PPP2R4 as a novel haploinsufficient tumor suppressor gene in multiple human cancers. These findings ultimately validate PP2A as a bona fide tumor suppressor and target for tumor suppressor reactivation therapies.

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Book
De schildklier

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