BIOMARKER COMBINATIONS IN EX VIVO LUNG PERFUSION (EVLP) PERFUSATE

    公开(公告)号:US20220341944A1

    公开(公告)日:2022-10-27

    申请号:US17413772

    申请日:2019-12-13

    Abstract: Methods and kits for screening, diagnosing, detecting or predicting a patient outcome/risk variable for a lung transplant recipient after transplant or an EVLP outcome by measuring biomarker levels of at least three biomarkers selected from IL-6, IL-8, IL-10 and IL-1β optionally in combination with one or both of sTNFR1 and sTREM1 in EVLP perfusate are described. The methods involve for example, i. obtaining one or more test EVLP perfusate samples of a donor lung; ii. determining in one or more test EVLP perfusate sample of a donor lung, a polypeptide level of the at least three biomarkers selected from IL-8, IL-6, IL-10 and IL-1β and optionally one or both of sTNFR1 and sTREM1 i; and iii. a) comparing the one or more parameter values related to a level of the at least three biomarkers in the perfusate sample with control EVLP data or a cut-off level, wherein the differential level is indicative of outcome/risk of after transplant or of an EVLP outcome; or b) using the one or more parameter values related to a level of the at least three biomarkers in combination, as part of an algebraic calculation or model of outcome/risk.

    BIOMARKERS FOR DETECTING OF OUTCOME/RISK OF THE PATIENTS WITH A RESPIRATORY ILLNESS

    公开(公告)号:US20230100616A1

    公开(公告)日:2023-03-30

    申请号:US17911208

    申请日:2021-03-15

    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-IRA, 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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