Macrocyclic Chelators and Methods of Use Thereof

    公开(公告)号:US20230110178A1

    公开(公告)日:2023-04-13

    申请号:US17810316

    申请日:2022-06-30

    Abstract: Macrocyclic chelators for chelation of alpha-emitting radiometal ions, such as actinium-225 are provided. Also provided are radiometal complexes containing an alpha-emitting radiometal ion bound to the macrocyclic chelator via coordinate bonding, and radioimmunoconjugates containing the radiometal complexes covalently linked to a targeting ligand, such as an antibody or antigen binding fragment thereof. The radioimmunoconjugates can be produced by click chemistry reactions. Methods of using the radiocomplexes and radioimmunoconjugates for selectively targeting neoplastic cells for radiotherapy and for treating neoplastic diseases and disorders are also described.

    Anti-CD3 antibodies and uses thereof

    公开(公告)号:US11603405B2

    公开(公告)日:2023-03-14

    申请号:US16418082

    申请日:2019-05-21

    Abstract: The present invention relates to antibodies that specifically bind CD3. The present invention relates to antibodies that specifically bind PSMA. The present invention relates to antibodies that specifically bind CD3 and PSMA. The present invention relates to antibodies that specifically bind IL1RAP. The present invention relates to antibodies that specifically bind CD33. The present invention relates to antibodies that specifically bind CD3 and IL1RAP. The present invention relates to antibodies that specifically bind CD3 and CD33. The present invention relates to antibodies that specifically bind TMEFF2. The present invention relates to antibodies that specifically bind CD3 and TMEFF2. The present invention relates to fragments of the antibodies, polynucleotides encoding the antibodies or fragments thereof, and methods of making and using the same.

    Method for Determining Severity of Skin Disease Based on Percentage of Body Surface Area Covered by Lesions

    公开(公告)号:US20230060162A1

    公开(公告)日:2023-03-02

    申请号:US18054372

    申请日:2022-11-10

    Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

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