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    SYSTEM AND METHOD FOR AUTOMATED FOCAL SOURCE DETECTION
    1.
    发明申请
    SYSTEM AND METHOD FOR AUTOMATED FOCAL SOURCE DETECTION 审中-公开

    公开(公告)号：WO2023004501A1

    公开(公告)日：2023-02-02

    申请号：PCT/CA2022/051145

    申请日：2022-07-25

    Applicant: UNIVERSITY HEALTH NETWORK

    Inventor： CHAUHAN, Vijay Singh ,  WANG, Bo ,  LIAO, Shun

    IPC: A61B5/316 ,  A61B5/00 ,  A61B5/318 ,  A61B5/361 ,  G06N3/04 ,  G06N3/08

    Abstract: Various embodiments are described herein for a system, method, and device for automated detection of focal source locations of electrophysiological activity in an organ. The system, method and device may also be used to guide catheter 5 ablation of the organ. An electrogram signal can be obtained from a location in the organ, and it can be determined if the electrogram is periodic. If so, the corresponding unipolar electrogram can be input to a deep learning neural network classification model trained to generate a unipolar electrogram classification result in response to receiving the unipolar electrogram as an input. The location can be identified as a focal 10 source location or a non-focal source location based on the unipolar electrogram classification result.

    BIOMARKERS FOR DETECTING OF OUTCOME/RISK OF THE PATIENTS WITH A RESPIRATORY ILLNESS
    2.
    发明申请
    BIOMARKERS FOR DETECTING OF OUTCOME/RISK OF THE PATIENTS WITH A RESPIRATORY ILLNESS 审中-公开

    公开(公告)号：WO2021179096A

    公开(公告)日：2021-09-16

    申请号：PCT/CA2021/050343

    申请日：2021-03-15

    Applicant: UNIVERSITY HEALTH NETWORK [CA]/[CA]

    Inventor： SAGE, Andrew ,  VALERO, Jerome ,  DEL SORBO, Lorenzo ,  CYPEL, Marcelo ,  WANG, Bo ,  KESHAVJEE, Shafique

    IPC: G01N33/48 ,  G01N33/53 ,  G01N33/543 ,  G16H50/30

    Abstract: Methods and kits for screening, diagnosing, detecting or predicting a patient outcome/risk in a patient with a respiratory illness, the method comprising: a. obtaining a sample obtained from the patient; b. quantitatively measuring in the sample a polypeptide level of one or more biomarkers selected from: IL-6, CXCL8, IL-10, IL-1RA, IL-2, IL-4, IL-7, IL-9, IL-13, IL-17, IFN-g, IP-10, MCP-1, G-CSF, GM-CSF, FGF-basic, SCGF-β, GRO-α, MIP1-α, MIP1-β, CK-18, PDGF-bb, caspase 3, HMGB-1, TNF α, VEGF, sTNFR1 and sTREM1; and c. i) comparing the level of the one or more biomarkers in the sample with a control or cut-off level, wherein the differential level is indicative of patient outcome risk; or ii) using the polypeptide level of several of the biomarkers in combination, as inputs for an algebraic calculation or machine learning model of patient outcome risk.

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