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Your search keyword '"Garini Yuval"' showing total 39 results
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39 results on '"Garini Yuval"'

38. Polyphosphate Reverses the Toxicity of the Quasi-Enzyme Bleomycin on Alveolar Endothelial Lung Cells In Vitro †.

Author

Müller, Werner E. G., Neufurth, Meik, Wang, Shunfeng, Schröder, Heinz C., Wang, Xiaohong, Mai, Sabine, Knecht, Hans, and Garini, Yuval

Subjects
PULMONARY alveoli, ENDOTHELIAL cells, IN vitro studies, BIOCHEMISTRY, IN vivo studies, COVID-19, DNA, COMBINATION drug therapy, LUNGS, ANTI-infective agents, CELL survival, PHENOMENOLOGY, CELL nuclei, GENE expression, DESCRIPTIVE statistics, TUMORS, BLEOMYCIN, PULMONARY fibrosis, CELL lines, PHOSPHATES, DRUG toxicity, PHARMACODYNAMICS
Abstract

Simple Summary: Bleomycin (BLM) is a medication introduced used to treat various types of cancer, including testicular cancer, ovarian cancer, and Hodgkin's disease. Its most serious side effect is pulmonary fibrosis and impaired lung function. Using A549 human lung cells it is shown that, in parallel to an increased cell toxicity and DNA damage, BLM causes a marked enlargement of the cell nucleus. This effect is abolished by inorganic polyphosphate (polyP), if this physiological polymer is administered together with BLM. The detoxification of BLM is–most likely–caused by the upregulation of the gene encoding the BLM hydrolase which inactivates BLM in vitro and in vivo. This study contributes also to a rational application in COVID-19 patients since polyP prevents binding of SARS-CoV-2 to host cells. The anti-cancer antitumor antibiotic bleomycin(s) (BLM) induces athyminic sites in DNA after its activation, a process that results in strand splitting. Here, using A549 human lung cells or BEAS-2B cells lunc cells, we show that the cell toxicity of BLM can be suppressed by addition of inorganic polyphosphate (polyP), a physiological polymer that accumulates and is released from platelets. BLM at a concentration of 20 µg ml−1 causes a decrease in cell viability (by ~70%), accompanied by an increased DNA damage and chromatin expansion (by amazingly 6-fold). Importantly, the BLM-caused effects on cell growth and DNA integrity are substantially suppressed by polyP. In parallel, the enlargement of the nuclei/chromatin in BLM-treated cells (diameter, 20–25 µm) is normalized to ~12 µm after co-incubation of the cells with BLM and polyP. A sequential application of the drugs (BLM for 3 days, followed by an exposure to polyP) does not cause this normalization. During co-incubation of BLM with polyP the gene for the BLM hydrolase is upregulated. It is concluded that by upregulating this enzyme polyP prevents the toxic side effects of BLM. These data might also contribute to an application of BLM in COVID-19 patients, since polyP inhibits binding of SARS-CoV-2 to cellular ACE2. [ABSTRACT FROM AUTHOR]

Published
2021
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39. Automatic classification of cancer cells in multispectral microscopic images of lymph node samples.

Author

Zimmerman-Moreno G, Marin I, Lindner M, Barshack I, Garini Y, Konen E, and Mayer A

Subjects
Automation, Cell Nucleus pathology, Female, Humans, Algorithms, Breast Neoplasms pathology, Image Processing, Computer-Assisted, Lymph Nodes pathology, Microscopy methods
Abstract

Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.

Published
2016
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