The Experts below are selected from a list of 9879 Experts worldwide ranked by ideXlab platform

Hiroyuki Mano - One of the best experts on this subject based on the ideXlab platform.

  • screening of genes responsible for differentiation of mouse mesenchymal stromal cells by dna Micro Array Analysis of c3h10t1 2 and c3h10t1 2 derived cell lines
    Cytotherapy, 2007
    Co-Authors: Katsutoshi Ozaki, Hiroyuki Mano, A. Miyazato, Akiko Meguro, Kazuo Muroi, Tadashi Nagai, Kazuya Sato, Keiya Ozawa
    Abstract:

    BackgroundThe molecular mechanisms underlying the biologic effects or differentiation of mesenchymal stromal cells (MSC) have not been clarified. Screening for genes differentially expressed at different stages is an important step in determining these molecular mechanisms.MethodsIn this study, we analyzed the gene expression profiles of C3H10T1/2 (10T1/2) cells and two sublines, A54 (pre-adipocyte) and M1601 (myoblast), as a model of MSC and downstream committed progenitors.ResultsWe found up-regulated expression of delta-like-1 (Dlk), Wnt-5a and IL-1 receptor-like-1 (ST2) in 10T1/2 cells; stem cell factor (SCF) and stromal derived factor-1 (SDF-1) in A54 cells; and cardiac muscle-specific gene in M1601 cells. Overexpression of Dlk in A54 cells did not induce any effects on their differentiation into adipocytes. After differentiation into adipocytes, A54 cells reduced the expression of SCF, SDF-1 and Ang-1 as well as the ability to support the formation of a cobblestone appearance.DiscussionThe results s...

  • Screening of genes responsible for differentiation of mouse mesenchymal stromal cells by DNA Micro-Array Analysis of C3H10T1/2 and C3H10T1/2-derived cell lines.
    Cytotherapy, 2007
    Co-Authors: Katsutoshi Ozaki, Hiroyuki Mano, A. Miyazato, Sato Kazuya, Akiko Meguro, Kazuo Muroi, Tadashi Nagai, Keiya Ozawa
    Abstract:

    BackgroundThe molecular mechanisms underlying the biologic effects or differentiation of mesenchymal stromal cells (MSC) have not been clarified. Screening for genes differentially expressed at different stages is an important step in determining these molecular mechanisms.MethodsIn this study, we analyzed the gene expression profiles of C3H10T1/2 (10T1/2) cells and two sublines, A54 (pre-adipocyte) and M1601 (myoblast), as a model of MSC and downstream committed progenitors.ResultsWe found up-regulated expression of delta-like-1 (Dlk), Wnt-5a and IL-1 receptor-like-1 (ST2) in 10T1/2 cells; stem cell factor (SCF) and stromal derived factor-1 (SDF-1) in A54 cells; and cardiac muscle-specific gene in M1601 cells. Overexpression of Dlk in A54 cells did not induce any effects on their differentiation into adipocytes. After differentiation into adipocytes, A54 cells reduced the expression of SCF, SDF-1 and Ang-1 as well as the ability to support the formation of a cobblestone appearance.DiscussionThe results s...

  • DNA Micro-Array Analysis of myelodysplastic syndrome.
    Leukemia & lymphoma, 2006
    Co-Authors: Hiroyuki Mano
    Abstract:

    Myelodysplastic syndrome (MDS) is an enigmatic disorder characterized by ineffective hematopoiesis and dysplastic morphology of blood cells. The clinical course of MDS consists of distinct stages, with early stages often progressing to advanced ones or to acute myeloid leukemia (AML). Little is known of the molecular pathogenesis of MDS or of the mechanism of its stage progression. DNA Micro-Array Analysis, which allows simultaneous monitoring of the expression levels of tens of thousands of genes, has the potential to provide insight into the pathophysiology of MDS. Several studies have applied this new technology to compare gene expression profiles either between MDS and the healthy condition, among the different stages of MDS or between MDS-derived AML and de novo AML. Selection of an appropriate hematopoietic fraction is important for such studies, which to date have been performed with differentiated granulocytes, CD34+ progenitors and CD133+ immature cells. These studies have revealed that each stag...

Joachim M. Buhmann - One of the best experts on this subject based on the ideXlab platform.

  • automated Analysis of tissue Micro Array images on the example of renal cell carcinoma
    Similarity-Based Pattern Analysis and Recognition, 2013
    Co-Authors: Peter J. Schüffler, Cheng Soon Ong, Volker Roth, Thomas Fuchs, Joachim M. Buhmann
    Abstract:

    Automated tissue Micro-Array Analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue Micro-Array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image Analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue Micro-Array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.

  • Similarity-Based Pattern Analysis and Recognition - Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
    Similarity-Based Pattern Analysis and Recognition, 2013
    Co-Authors: Peter J. Schüffler, Cheng Soon Ong, Volker Roth, Thomas Fuchs, Joachim M. Buhmann
    Abstract:

    Automated tissue Micro-Array Analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue Micro-Array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image Analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue Micro-Array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.

  • DAGM-Symposium - Computational TMA Analysis and cell nucleus classification of renal cell carcinoma
    Lecture Notes in Computer Science, 2010
    Co-Authors: Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann
    Abstract:

    We consider an automated processing pipeline for tissue Micro Array Analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.

Theresa P. Pretlow - One of the best experts on this subject based on the ideXlab platform.

Peter J. Schüffler - One of the best experts on this subject based on the ideXlab platform.

  • automated Analysis of tissue Micro Array images on the example of renal cell carcinoma
    Similarity-Based Pattern Analysis and Recognition, 2013
    Co-Authors: Peter J. Schüffler, Cheng Soon Ong, Volker Roth, Thomas Fuchs, Joachim M. Buhmann
    Abstract:

    Automated tissue Micro-Array Analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue Micro-Array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image Analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue Micro-Array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.

  • Similarity-Based Pattern Analysis and Recognition - Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
    Similarity-Based Pattern Analysis and Recognition, 2013
    Co-Authors: Peter J. Schüffler, Cheng Soon Ong, Volker Roth, Thomas Fuchs, Joachim M. Buhmann
    Abstract:

    Automated tissue Micro-Array Analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue Micro-Array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image Analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue Micro-Array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.

  • DAGM-Symposium - Computational TMA Analysis and cell nucleus classification of renal cell carcinoma
    Lecture Notes in Computer Science, 2010
    Co-Authors: Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann
    Abstract:

    We consider an automated processing pipeline for tissue Micro Array Analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.

Aristides Maltez Filho - One of the best experts on this subject based on the ideXlab platform.

  • Transcriptional Complementarity in Breast Cancer: Application to Detection of Circulating Tumor Cells
    Molecular Diagnosis, 2001
    Co-Authors: Raymond L. Houghton, Davin C. Dillon, David A. Molesh, Barbara K. Zehentner, John Jiang, Cheryl Schmidt, Anthony Frudakis, Elizabeth A. Repasky, Jiangchun Xu, Aristides Maltez Filho
    Abstract:

    Background: We used a combination of genetic subtraction, silicon DNA Micro-Array Analysis, and quantitative PCR to identify tissue-and tumor-specific genes as diagnostic targets for breast cancer. Methods and Results: From a large number of candidate antigens, several specific subsets of genes were identified that showed concordant and complementary expression profiles. Whereas transcriptional profiling of mammaglobin resulted in the detection of 70% of tumors in a panel of 46 primary and metastatic breast cancers, the inclusion of three additional markers resulted in detection of all 46 specimens. Immunomagnetic epithelial cell enrichment of circulating tumor cells from the peripheral blood of patients with metastatic breast cancer, coupled with RT-PCR-based amplification of breast tumor—specific transcripts, resulted in the detection of anchorage-independent tumor cells in the majority of patients with breast cancer with known metastatic disease. Conclusion: Complementation of mammaglobin with three additional genes in RT-PCR increases the detection of breast cancers in tissue and circulating tumor cells.

  • Transcriptional complementarity in breast cancer: application to detection of circulating tumor cells.
    Molecular diagnosis : a journal devoted to the understanding of human disease through the clinical application of molecular biology, 2001
    Co-Authors: Raymond L. Houghton, Davin C. Dillon, David A. Molesh, Barbara K. Zehentner, John Jiang, Cheryl Schmidt, Anthony Frudakis, Elizabeth A. Repasky, Aristides Maltez Filho
    Abstract:

    Background: We used a combination of genetic subtraction, silicon DNA Micro-Array Analysis, and quantitative PCR to identify tissue-and tumor-specific genes as diagnostic targets for breast cancer.