AI Article Synopsis
- * An extensive 848,192-image database from antibody production was analyzed using a convolutional neural network, chosen for its effectiveness in real-time object detection.
- * The system allows monitoring of different cell death types, including viable, necrotic, and apoptotic cells, during hybridoma growth under various conditions, marking a significant advancement in process analytical tools for research.
Article Abstract
An in situ microscope based on pulsed transmitted light illumination via optical fiber was combined to artificial-intelligence to enable for the first time an online cell classification according to well-known cellular morphological features. A 848 192-image database generated during a lab-scale production process of antibodies was processed using a convolutional neural network approach chosen for its accurate real-time object detection capabilities. In order to induce different cell death routes, hybridomas were grown in normal or suboptimal conditions in a stirred tank reactor, in the presence of substrate limitation, medium addition, pH regulation problem or oxygen depletion. Using such an optical system made it possible to monitor real-time the evolution of different classes of animal cells, among which viable, necrotic and apoptotic cells. A class of viable cells displaying bulges in feast or famine conditions was also revealed. Considered as a breakthrough in the catalogue of process analytical tools, in situ microscopy powered by artificial-intelligence is also of great interest for research.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10716407 | PMC |
| http://dx.doi.org/10.1038/s41598-023-48733-x | DOI Listing |
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