FAP-TARGETED RADIOPHARMACEUTICALS AND IMAGING AGENTS, AND USES RELATED THERETO

    公开(公告)号:US20220370647A1

    公开(公告)日:2022-11-24

    申请号:US17211481

    申请日:2021-03-24

    IPC分类号: A61K51/04

    摘要: The tumor stroma, which accounts for a large part of the tumor mass, represents an attractive target for the delivery of diagnostic and therapeutic compounds. Here, the focus is notably on a subpopulation of stromal cells, known as cancer-associated fibroblasts, which are present in more than 90% of epithelial carcinomas, including pancreatic, colon, and breast cancer. Cancer-associated fibroblasts feature high expression of FAP, which is not detectable in adult normal tissue but is associated with a poor prognosis in cancer patients. The present invention provides small-molecule radiopharmaceutical and imaging agents based on a FAP-specific inhibitor.

    SYSTEMS AND METHODS FOR HIGH-THROUGHPUT SCREENING AND ANALYSIS OF DRUG DELIVERY SYSTEMS IN VITRO

    公开(公告)号:US20220299494A1

    公开(公告)日:2022-09-22

    申请号:US17753312

    申请日:2020-08-28

    摘要: The present disclosure provides a method for screening drug delivery vehicles for use in delivering cargo via oral delivery. The method includes introducing a drug delivery vehicle comprising an imaging agent into a lumen of an artificial intestine system composed of a scaffold matrix material. The scaffold matrix material includes an interconnected network of pores, intestinal epithelial cells positioned on an inner surface of the lumen, and human-based cells positioned within the pores and surrounding the intestinal epithelial cells. The method includes maintaining the artificial intestine system in physiologically relevant conditions for a predetermined length of time, and detecting a color change induced by the imaging agent within at least a portion of the human-based cells.

    Dual-slope Method for Enhanced Depth Sensitivity in Diffuse Optical Spectroscopy

    公开(公告)号:US20220218267A1

    公开(公告)日:2022-07-14

    申请号:US17617654

    申请日:2020-06-12

    摘要: An apparatus for earning out near-infrared spectroscopy using intensity-modulated near-infrared radiation or pulsed near-infrared radiation includes sources and detectors. For each source, there exists first and second distances. The first distance is a distance between the source and a first detector. The second distance is a distance between the source and the second detector. For each source, the difference between these two distances is the same. Additionally, wherein, for each source, the detector at a shorter distance is the same detector that is at a longer distance for the other source. A processor derives, from signals received by the detectors, a parameter indicative of two matched slopes. Tins parameter is either phase of the intensity-modulated near-infrared radiation or mean time-of-flight data for the pulsed near-infrared radiation. The processor then provides output data based on an average of the matched slopes. This promotes reduced sensitivity to superficial layers and enhanced sensitivity to deeper portions of a medium that is under investigation.

    Tensor-based predictions from analysis of time-varying graphs

    公开(公告)号:US11386507B2

    公开(公告)日:2022-07-12

    申请号:US16579454

    申请日:2019-09-23

    摘要: A computer-implemented method for analyzing a time-varying graph is provided. The time-varying graph includes nodes representing elements in a network, edges representing transactions between elements, and data associated with the nodes and the edges. The computer-implemented method includes constructing, using a processor, adjacency and feature matrices describing each node and edge of each time-varying graph for stacking into an adjacency tensor and describing the data of each time-varying graph for stacking into a feature tensor, respectively. The adjacency and feature tensors are partitioned into adjacency and feature training tensors and into adjacency and feature validation tensors, respectively. An embedding model and a prediction model are created using the adjacency and feature training tensors. The embedding and prediction models are validated using the adjacency and feature validation tensors to identify an optimized embedding-prediction model pair.