Nantbio, Inc.

Nantcell, Inc.

Krsto Sbutega

Kayvan R Niazi

Patrick Soon-Shiong

Nicholas James Witchey

Bing Song

Peter Sieling

Adam D Lazar

Albert Wu

A computer-implemented method of using interferometry to detect mass changes of objects in a solution includes obtaining a time series of images using interferometry, and performing background correction on each image by classifying pixels of the image as background pixels or object pixels, fitting only the background pixels of the image to a function to generate a background fitted function, and subtracting the background fitted function from the image to generate a background corrected image. The method includes performing segmentation on the background corrected images to resolve boundaries of one or more objects, performing motion tracking on the objects to track changes in position of the objects, determining respective masses of the motion tracked objects and determining, for each image in the time series, an aggregate mass based on the respective masses to determine whether the aggregate mass of the motion tracked objects is increasing or decreasing.

Interferometry based systems to detect small mass changes of an object in a solution

Provided herein are methods of in-vitro primary T cell growth that enrich T cells in a blood sample, stimulate the T cells with anti-CD2, anti-CD3, and/or anti-CD28 and that expand the T cells with a cytokine. Also provided are methods of treating a tumor in a patient using the expanded T cells.

Primary T-cell expansion

An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.

Systems and methods for performing segmentation based on tensor inputs

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Redondo Beach, CA, United States of America

Krsto Sbutega