5/17/2023 0 Comments Prism ic50![]() In this context, automatic analysis of drug responsiveness using cell images have facilitated advanced microscopic technology such as counting cell numbers and discriminating between live and dead cells to determine in vitro responses 1, 2. In vitro high-throughput assays for screening drug responsiveness are increasingly needed not only for medical and pharmaceutical research, but also for clinical purposes using cell lines, patient-derived primary cells, or organoids. Our models could be applied to label-free cells to conduct rapid and large-scale tests while minimizing cost and labor, such as high-throughput screening for drug responsiveness. When drug responsiveness was estimated using allogenic models that were trained with a different cell type, the correlation coefficient decreased to 0.004085–0.8643. In addition, the measured and predicted IC50 values were not statistically different. The linear relationship coefficient (r 2) between measured and predicted cell viability was determined as 0.94–0.95 for the three cell types. ![]() Convolutional neural network (CNN) models for the study cells were constructed using augmented image data the predicted cell viability using CNN models was compared to the cell viability measured by colorimetric assay. ![]() ![]() The microscopic images of these cells following exposure to various concentrations of DOX were trained with the measured value of cell viability using a colorimetric cell proliferation assay. In this study, A549, HEK293, and NCI-H1975 cells were cultured, each of which have different molecular shapes and levels of drug responsiveness to doxorubicin (DOX). To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unlabeled cell culture images captured by a light microscope as input.
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