Application Note: Bioprinting Beyond the Limitations of Traditional Cancer Model
M-Series Application Note: Bioprinting Beyond the Limitations of Traditional Cancer Models View Paper
During this webinar, Dr. Parkins will review automated, time-lapse microscopy and image-based cell counting and discuss generating high-quality and robust data using the CytoSMART products offered by Scintica. The system specifications, potential applications, and example data will be discussed for the Lux2, the Lux3 FL, and the OMNI live cell imaging systems.
CytoSMART is an innovator in kinetic live-cell imaging. Combining compact and fast imaging hardware with powerful image analysis algorithms supported by cloud computing. Automation in time-lapse microscopy and image-based cell counting to generate high-quality and robust data.
Their team of engineers continues to develop and optimize the image analysis and data storage capacities linked to their systems, making sure that data sets are easily processed, stored, and kept securely in an online environment.
The CytoSMART Lux2 is a highly compact, easy-to-use, and affordable inverted microscope for bright-field live-cell imaging so it can be used in every biological laboratory. While it has functionality for basic imaging, it also has the capability to be used in routine cell culture processes like tracking confluency over time.
The CytoSMART Lux3 FL fluorescent cell imaging device allows researchers to track dynamic cellular processes by taking high-quality images to create real-time time-lapse movies. Simultaneously, the cells can be kept in a controlled environment inside a standard cell culture incubator.
The OMNI has been developed as an automated bright-field lab microscope that visualizes whole culture vessels and can even be used within a standard CO2-incubator. With the Omni, you can perform kinetic assays by creating time-lapse videos that depict cell behavior for days or weeks at a time.
Learning objectives:
M-Series Application Note: Bioprinting Beyond the Limitations of Traditional Cancer Models View Paper
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