COMPOSITION FOR DIAGNOSIS, PREVENTION, OR TREATMENT OF VASCULAR SMOOTH MUSCLE CELL PROLIFERATIVE DISEASE, USING MIRNA INHIBITOR

    公开(公告)号:US20250002916A1

    公开(公告)日:2025-01-02

    申请号:US18708953

    申请日:2022-11-09

    Abstract: The present invention relates to a pharmaceutical composition comprising a microRNA (miRNA) inhibitor as an active ingredient for the prevention or treatment of a vascular smooth muscle cell proliferative disease; a kit comprising a miRNA-detectable agent for the diagnosis of a vascular smooth muscle cell proliferative disease; a method for providing information for the diagnosis of the diseases; and a therapeutic method. Discovered in the present invention were the miRNA subsets miR-132-3p, miR-370-3p, miR-130b-5p, and miR-410-3p that are involved in proliferative diseases of smooth muscle cells. It was found that these miRNAs significantly increased their expression 5-fold or more in damaged arteries, compared to the control, and in particular, miR-370-3p was highly expressed in coronary tissues of atherosclerosis patients. Thus, the miRNAs can be advantageously used for diagnosing diseases related thereto and further can find applications in treating vascular smooth muscle cell proliferative diseases.

    Steering wheel for vehicle
    63.
    外观设计

    公开(公告)号:USD1047829S1

    公开(公告)日:2024-10-22

    申请号:US29793285

    申请日:2022-03-04

    Abstract: FIG. 1 is a front perspective view of a steering wheel for vehicle showing our new design in a first state in which a central portion of the steering wheel for vehicle is in a first position;
    FIG. 2 is a front view thereof;
    FIG. 3 is a rear view thereof;
    FIG. 4 is a left side view thereof;
    FIG. 5 is a right side view thereof;
    FIG. 6 is a top plan view thereof;
    FIG. 7 is a bottom plan view thereof;
    FIG. 8 is a front perspective view thereof in a second state in which the central portion of the steering wheel for vehicle is rotated to a second position;
    FIG. 9 is a front view thereof;
    FIG. 10 is a rear view thereof;
    FIG. 11 is a left side view thereof;
    FIG. 12 is a right side view thereof;
    FIG. 13 is a top plan view thereof;
    FIG. 14 is a bottom plan view thereof;
    FIG. 15 is a front perspective view thereof in a third state in which a screen unit on the rotated central portion of the steering wheel for vehicle protrudes in the form of a keypad;
    FIG. 16 is a front view thereof;
    FIG. 17 is a rear view thereof;
    FIG. 18 is a left side view thereof;
    FIG. 19 is a right side view thereof;
    FIG. 20 is a top plan view thereof; and,
    FIG. 21 is a bottom plan view thereof.
    The broken lines depict portions of the steering wheel for vehicle that form no part of the claimed design.
    The present design is a dynamic design and can be deformed from a first state to a second state in which the central portion of the steering wheel is rotated and to a third state in which the rotated central portion of the steering wheel protrudes in the form of a keypad.

    ANALYSIS DEVICE AND METHOD FOR DETECTING VARIABLE VULNERABILITY IN SOFTWARE USING MACHINE LEARNING MODEL

    公开(公告)号:US20240202099A1

    公开(公告)日:2024-06-20

    申请号:US18368833

    申请日:2023-09-15

    CPC classification number: G06F11/3612 G06F11/3624 G06F11/3636

    Abstract: Provided are a device and method for detecting a variable vulnerability in software using a machine learning (ML) model. The method performed by an analysis device includes receiving a source code of a program to be analyzed, replacing call functions, variable names, and call stack functions in an execution log generated during execution of the source code with certain identifiers (IDs) to preprocess the execution log, analyzing the preprocessed execution log through a pretrained first learning model to classify whether each pair of a global variable and a call function is at an initialization location, analyzing the preprocessed execution log through a pretrained second learning model to estimate a maximum value and a minimum value of the global variable, and determining whether the global variable is vulnerable on the basis of information output by the first learning model and information output by the second learning model.

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