Fast Artificial Intelligence for the Detection of Viral Particle Proliferation in Cellular Imaging Data

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dc.contributor.author Birgitta Dresp-Langley
dc.contributor.author Wandeto, John Mwangi
dc.date.accessioned 2022-08-19T07:08:39Z
dc.date.available 2022-08-19T07:08:39Z
dc.date.issued 2022-08
dc.identifier.uri http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/6280
dc.description.abstract Immunocytochemistry and cell viability imaging by labeling techniques permit studying spatial and temporal dynamics of virusspreading on host cell surfaces in vitro. Haseltine et al. [1] developed an imaging method that permits tracking the spread of focal viral infection using a combination of immunocytochemical labeling and step-by-step digital imaging. Baby hamster kidney (BHK) cells were seeded on six well plates, grown as confluent monolayers and covered with a thin layer of agar. After piercing a small orifice in the agar, cell layers were infected by injecting 5 μl of virus VSVN1 inoculum with 1.6x107 infectious particles (a multiplicity of infection of 20). Cells were subsequently fixed, and immunofluorescence labeled with an antibody against a viral glycoprotein on and within the infected cells. en_US
dc.language.iso en en_US
dc.publisher European Society for Medecine General Assembly en_US
dc.title Fast Artificial Intelligence for the Detection of Viral Particle Proliferation in Cellular Imaging Data en_US
dc.type Article en_US


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