HIGH THROUGHPUT CELL MIGRATION ASSAY PLATES AND METHODS OF FABRICATION

    公开(公告)号:US20230398539A1

    公开(公告)日:2023-12-14

    申请号:US18249864

    申请日:2021-10-22

    Abstract: A Cell Migration Assay Plates (CMAP) assembly for high throughput microfluidic migration assays and method of manufacturing thereof are provided. The CMAP assembly includes a top plate having a plurality of wells aligned with a bottom plate having a plurality of troughs. Each of the plurality of wells is defined at least in part by first and second reservoirs and a divisional wall extending between the reservoirs. The bottom plate is secured to the top plate to form a plurality of micro-channels, such that each one of the plurality of micro-channels is defined by a portion of one of the divisional walls and a portion of a corresponding one of the plurality of troughs. The plurality of micro-channels enable communication between the reservoirs and visualization of cells migrating through the micro-channels. In this manner, migration of cells through the micro-channels can be visualized for testing and screening applications.

    DEEP LEARNING SYSTEM FOR PREDICTING THE T CELL RECEPTOR BINDING SPECIFICITY OF NEOANTIGENS

    公开(公告)号:US20230349914A1

    公开(公告)日:2023-11-02

    申请号:US18029395

    申请日:2021-09-30

    Inventor: Tianshi LU Tao WANG

    CPC classification number: G01N33/6845 G06N3/08 G06N3/0455 G16B40/20 G16B15/30

    Abstract: Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). To help determine the TCRs that interact with particular neoantigens, prediction models that predict TCR-binding specificities of neoantigens presented by different classes of major histocompatibility complex (MHCs) were developed. To confirm the applicability of the model to clinical settings, the prediction models were comprehensively validated by a series of analyses. The validated prediction models used a flexible transfer learning approach and differential learning schema to achieve highly accurate prediction of TCR binding specificity only using TCR sequence data, antigen sequence data, and MHC alleles.

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