Abstract: A method for characterizing cancer organoid response to an immune cell based therapy, includes providing a panel of different combinations of cancer organoid cells and immune cells to culturing wells and culturing the different combination under conditions that support organoid growth. Brightfield and corresponding fluorescence images of the culturing wells are captured and provided to one or more trained machine learning algorithms that identify and distinguish cancer organoid cells from immune cells and characterize cancer organoid morphology changes caused by an immune cell based therapies, from which an analytical report including a characterization of cancer organoid cell death caused by the immune cell based therapy is provided.

Abstract: A computer-implemented method, computing system and computer-readable medium include receiving training data and training a machine learning model to generate a cell expression map. A computer-implemented method, computing system and computer-readable medium includes receiving a histology image and a cell segmentation map and processing them using a trained machine learning model.

Abstract: A method, computing system, and computer-readable medium may receive and process de novo quantitative measures input using a trained machine learning model to generate one or more predicted experimental cell quantitative measures. A method, computing system, and computer-readable medium may receive training quantitative measures data; train a machine learning model to generate predicted experimental cell viability measures based on a de novo quantitative measures input, by processing the training quantitative measures data; and store the machine learning model as a trained machine learning model in a memory of a computer.

Abstract: A computer-implemented method, computing system and computer-readable medium include receiving training data and training a machine learning model to generate a cell expression map. A computer-implemented method, computing system and computer-readable medium includes receiving a histology image and a cell segmentation map and processing them using a trained machine learning model.

Abstract: A method for characterizing cancer organoid response to an immune cell based therapy, includes providing a panel of different combinations of cancer organoid cells and immune cells to culturing wells and culturing the different combination under conditions that support organoid growth. Brightfield and corresponding fluorescence images of the culturing wells are captured and provided to one or more trained machine learning algorithms that identify and distinguish cancer organoid cells from immune cells and characterize cancer organoid morphology changes caused by an immune cell based therapies, from which an analytical report including a characterization of cancer organoid cell death caused by the immune cell based therapy is provided.

Abstract: Methods and devices are disclosed for selective photo-destruction of one chiral enantiomer of a compound using nanostructures by enhancing differential absorption of circularly polarized light by the one chiral enantiomer. Methods and devices are disclosed for selective enrichment of one chiral enantiomer of a compound using nanostructures by enhancing differential absorption of circularly polarized light by the one chiral enantiomer. The nanostructures support optical frequency electric resonances and optical frequency magnetic resonances.

Abstract: Methods and devices are disclosed for selective photo-destruction of one chiral enantiomer of a compound using nanostructures by enhancing differential absorption of circularly polarized light by the one chiral enantiomer. Methods and devices are disclosed for selective enrichment of one chiral enantiomer of a compound using nanostructures by enhancing differential absorption of circularly polarized light by the one chiral enantiomer. The nanostructures support optical frequency electric resonances and optical frequency magnetic resonances.

Abstract: Methods and devices are disclosed for selective photo-destruction of one chiral enantiomer of a compound using nanostructures by enhancing differential absorption of circularly polarized light by the one chiral enantiomer. Methods and devices are disclosed for selective enrichment of one chiral enantiomer of a compound using nanostructures by enhancing differential absorption of circularly polarized light by the one chiral enantiomer. The nanostructures support optical frequency electric resonances and optical frequency magnetic resonances.