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  1. Article: Chemokine signaling in cancer-stroma communications.

    Singh, Arun J / Gray, Joe W

    Journal of cell communication and signaling

    2021  Volume 15, Issue 3, Page(s) 361–381

    Abstract: Cancer is a multi-faceted disease in which spontaneous mutation(s) in a cell leads to the growth and development of a malignant new organ that if left undisturbed will grow in size and lead to eventual death of the organism. During this process, multiple ...

    Abstract Cancer is a multi-faceted disease in which spontaneous mutation(s) in a cell leads to the growth and development of a malignant new organ that if left undisturbed will grow in size and lead to eventual death of the organism. During this process, multiple cell types are continuously releasing signaling molecules into the microenvironment, which results in a tangled web of communication that both attracts new cell types into and reshapes the tumor microenvironment as a whole. One prominent class of molecules, chemokines, bind to specific receptors and trigger directional, chemotactic movement in the receiving cell. Chemokines and their receptors have been demonstrated to be expressed by almost all cell types in the tumor microenvironment, including epithelial, immune, mesenchymal, endothelial, and other stromal cells. This results in chemokines playing multifaceted roles in facilitating context-dependent intercellular communications. Recent research has started to shed light on these ligands and receptors in a cancer-specific context, including cell-type specificity and drug targetability. In this review, we summarize the latest research with regards to chemokines in facilitating communication between different cell types in the tumor microenvironment.
    Language English
    Publishing date 2021-06-04
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 2299380-0
    ISSN 1873-961X ; 1873-9601
    ISSN (online) 1873-961X
    ISSN 1873-9601
    DOI 10.1007/s12079-021-00621-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book: Flow cytogenetics

    Gray, Joe W.

    (Analytical cytology series)

    1989  

    Author's details ed. by Joe W. Gray
    Series title Analytical cytology series
    Keywords Flow Cytometry ; Chromosome Mapping ; Chromosomes / chemistry ; Cytogenetik ; Durchflusscytometrie
    Subject Flow cytometry ; Durchflusszytometrie ; Flowzytometrie ; Zellgenetik ; Zytogenetik
    Language English
    Size XVI, 312 S. : Ill., graph. Darst.
    Publisher Academic Pr
    Publishing place London u.a.
    Publishing country Great Britain
    Document type Book
    HBZ-ID HT003570897
    ISBN 0-12-296110-2 ; 978-0-12-296110-6
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    1990 A 4534 order for loan Magazin

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  3. Article ; Online: PI3 Kinase Pathway Mutations in Human Cancers.

    Gray, Joe W

    JAMA oncology

    2016  Volume 2, Issue 12, Page(s) 1543–1544

    MeSH term(s) Aminopyridines/therapeutic use ; Class I Phosphatidylinositol 3-Kinases ; Humans ; Isoquinolines/therapeutic use ; Molecular Targeted Therapy ; Morpholines/therapeutic use ; Mutation ; Neoplasms/drug therapy ; Neoplasms/genetics ; Neoplasms/pathology ; PTEN Phosphohydrolase/antagonists & inhibitors ; PTEN Phosphohydrolase/genetics ; Phosphatidylinositol 3-Kinases/antagonists & inhibitors ; Phosphatidylinositol 3-Kinases/genetics ; Proto-Oncogene Proteins c-akt/antagonists & inhibitors ; Proto-Oncogene Proteins c-akt/genetics ; Purines/therapeutic use ; Quinazolinones/therapeutic use ; Signal Transduction ; Tumor Suppressor Protein p53/genetics
    Chemical Substances Aminopyridines ; IPI-145 ; Isoquinolines ; Morpholines ; NVP-BKM120 ; Purines ; Quinazolinones ; TP53 protein, human ; Tumor Suppressor Protein p53 ; Phosphatidylinositol 3-Kinases (EC 2.7.1.-) ; Class I Phosphatidylinositol 3-Kinases (EC 2.7.1.137) ; PIK3CA protein, human (EC 2.7.1.137) ; AKT1 protein, human (EC 2.7.11.1) ; Proto-Oncogene Proteins c-akt (EC 2.7.11.1) ; PTEN Phosphohydrolase (EC 3.1.3.67) ; PTEN protein, human (EC 3.1.3.67) ; idelalisib (YG57I8T5M0)
    Language English
    Publishing date 2016-12-01
    Publishing country United States
    Document type Journal Article
    ISSN 2374-2445
    ISSN (online) 2374-2445
    DOI 10.1001/jamaoncol.2016.0875
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book: Techniques in cell cycle analysis

    Gray, Joe W.

    (Biological methods)

    1987  

    Author's details ed. by Joe W. Gray
    Series title Biological methods
    Keywords Cell Cycle ; Cytological Techniques ; Zellwachstum ; Zellzyklus ; Methode
    Subject Methodik ; Verfahren ; Technik ; Methoden ; Mitosezyklus ; Zellcyclus
    Size XVIII, 407 S. : Ill., graph. Darst.
    Publisher Humana Pr
    Publishing place Clifton, NJ
    Publishing country United States
    Document type Book
    HBZ-ID HT003118045
    ISBN 0-89603-097-0 ; 978-0-89603-097-8
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    1988 A 2458 order for loan Magazin

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  5. Article ; Online: Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

    Pagano, Lucas / Thibault, Guillaume / Bousselham, Walid / Riesterer, Jessica L / Song, Xubo / Gray, Joe W

    Frontiers in bioinformatics

    2023  Volume 3, Page(s) 1308707

    Abstract: Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual ... ...

    Abstract Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, and semi-supervised learning as well as the next steps for the mitigation of the manual segmentation bottleneck.
    Language English
    Publishing date 2023-12-15
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2023.1308707
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

    Pagano, Lucas / Thibault, Guillaume / Bousselham, Walid / Riesterer, Jessica L / Song, Xubo / Gray, Joe W

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual ... ...

    Abstract Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, semi-supervised learning as well as next steps for the mitigation of the manual segmentation bottleneck.
    Language English
    Publishing date 2023-11-01
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.10.30.563998
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book: Monoclonal antibodies against bromodeoxyuridine

    Gray, Joe W.

    1985  

    Author's details ed. Joe W. Gray
    Keywords Antibodies, Monoclonal ; Bromodeoxyuridine / analysis
    Size VIII, S. 499 - 671 : Ill., graph. Darst.
    Publisher Liss
    Publishing place New York
    Publishing country United States
    Document type Book
    Note Aus: Cytometry. - 6 (1985), S. 499 - 671
    HBZ-ID HT002923199
    ISBN 0-8451-4206-2 ; 978-0-8451-4206-6
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    1987 A 1354 order for loan Magazin

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  8. Article ; Online: Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays.

    Ternes, Luke / Lin, Jia-Ren / Chen, Yu-An / Gray, Joe W / Chang, Young Hwan

    PLoS computational biology

    2022  Volume 18, Issue 9, Page(s) e1010505

    Abstract: Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still ... ...

    Abstract Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss, and long imaging acquisition time. As such, selecting which markers to include on a panel is important, since removing important markers will result in a loss of biologically relevant information, but identifying redundant markers will provide a room for other markers. To address this, we propose computational approaches to determine the amount of shared information between markers and select an optimally reduced panel that captures maximum amount of information with the fewest markers. Here we examine several panel selection approaches and evaluate them based on their ability to reconstruct the full panel images and information within breast cancer tissue microarray datasets using cyclic immunofluorescence as a proof of concept. We show that all methods perform adequately and can re-capture cell types using only 18 of 25 markers (72% of the original panel size). The correlation-based selection methods achieved the best single-cell marker mean intensity predictions with a Spearman correlation of 0.90 with the reduced panel. Using the proposed methods shown here, it is possible for researchers to design more efficient multiplex imaging panels that maximize the amount of information retained with the limited number of markers with respect to certain evaluation metrics and architecture biases.
    MeSH term(s) Artifacts ; Biomarkers ; Breast Neoplasms ; Female ; Humans
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-09-30
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010505
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book: Analytical cytogenetics

    Latt, Samuel A. / Gray, Joe W.

    dedicated to the memory of Sam Latt ; [org. ... for the 13th international meeting of the Society for Analytical Cytology, Breckenridge, Colo., Sept. 4 - 9, 1988]

    (Cytometry ; 11,1 = Spec. issue)

    1990  

    Institution Society for Analytical Cytology
    Author's details guest eds. Joe W. Gray
    Series title Cytometry ; 11,1 = Spec. issue
    Keywords Cytogenetics / congresses
    Size XXIV S., S. 1 - 218 : Ill., graph. Darst.
    Publisher Wiley-Liss
    Publishing place New York, NY
    Publishing country United States
    Document type Book
    HBZ-ID HT003496957
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 1688 -11- verfügbar in Königswinter Königswinter

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  10. Article ; Online: 3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding.

    Burlingame, Erik / Ternes, Luke / Lin, Jia-Ren / Chen, Yu-An / Kim, Eun Na / Gray, Joe W / Chang, Young Hwan

    Frontiers in bioinformatics

    2023  Volume 3, Page(s) 1275402

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2023-10-19
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2023.1275402
    Database MEDical Literature Analysis and Retrieval System OnLINE

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