Abstract

Disclosed is three-dimensional interferometric lattice light-sheet (3D-iLLS) imaging, an approach that overcomes limitations of prior microscopy techniques. 3D-iLLS provides, by virtue of selective-plane illumination (SPIM), low light levels and photobleaching, while providing increased background suppression and significantly improved volumetric imaging/sectioning capabilities through 4Pi interferometry. An example setup demonstrated 3D-iLLS with axial resolution and single-particle localization precision down to <100 nm (FWHM) and <10 nm (16), respectively. 3D-iLLS enables a fuller elucidation of sub-cellular phenomena by enhanced 4D resolution and improved SNR during live imaging.

IPC Classes  ?

• G01N 21/64 - FluorescencePhosphorescence
• G02B 21/08 - Condensers
• G02B 21/16 - Microscopes adapted for ultraviolet illumination

Abstract

Provided are age-modulated cells and method for making age-modulated cells. The aging and rejuvenation processes can be induced for young, aged, mature and/or immature cells, such as a somatic cell, a stem cell, a stem cell-derived somatic cell, including an induced pluripotent stem cell-derived cell, by contacting cells with one or more age-inducing or rejuvenating agent. Methods described by the present disclosure can produce age-appropriate cells from a somatic cell or a stem cell, such as an old cell, young cell, immature cell, and/or a mature cell. Such age-modified cells constitute model systems for the study of late-onset diseases and/or disorders.

IPC Classes  ?

• C12N 5/077 - Mesenchymal cells, e.g. bone cells, cartilage cells, marrow stromal cells, fat cells or muscle cells
• C12N 5/0793 - Neurons

Abstract

Presented herein are systems and systems of determining values indicating probabilities of conditions of subjects using blood samples. A computing system may receive a plurality of images of a blood sample having white blood cells (WBCs) and red blood cells (RBCs) obtained from a subject at a time. The computing system may identify, from the plurality of images of the blood sample, (i) a set of WBC images corresponding to the WBCs and (ii) a set of RBC images corresponding to the RBCs. The computing system may apply the set of WBC images and the set of RBC images to a machine learning (ML) architecture. The computing system may determine, based on applying the ML architecture, a value indicating a probability of mortality for the subject at the time interval relative to the time. The computing system may generate a classification of the first subject in accordance with the value.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G06N 20/20 - Ensemble learning
• G16B 40/20 - Supervised data analysis

Abstract

Disclosed herein are methods including LATS kinase inhibitors or TIP60 inhibitors to perturb lineages within heterogenous tumors (e.g., prostate cancer) and enhance responsiveness to Androgen Receptor Signaling Inhibitor (ARSI) therapy or other therapeutic agents that target adenocarcinoma lineages (e.g., PSMA or STEAP1-expressing tumor cells.

Abstract

The presently disclosed subject matter provides compositions and methods for targeting immune responses toward tumor antigen-bearing cells. It relates to compositions, e.g., modified immunoresponsive cells, comprising an antigen-recognizing receptor (e.g., a chimeric antigen receptor (CAR)) comprising a pTCR polypeptide. These cells have improved activity and/or efficiency.

IPC Classes  ?

• C07K 14/725 - T-cell receptors
• A61K 40/31 - Chimeric antigen receptors [CAR]
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents

Abstract

The present disclosure is directed to systems for inhibiting NFAT and/or NFkB signaling to improve the function of CAR expressing immune cells, e.g., CAR T cells. The system enables generation of immune cells engineered to express a CAR or multiple combinations of CARs (multi-CAR) and inhibitors of NFAT and/or NFkB signaling.

IPC Classes  ?

• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61P 35/02 - Antineoplastic agents specific for leukemia
• C07K 7/08 - Linear peptides containing only normal peptide links having 12 to 20 amino acids
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 9/10 - Transferases (2.)
• C12N 15/86 - Viral vectors

Abstract

Described herein are compositions, uses thereof, and methods for treating a viral infection in a host cell or organism infected by the virus, such as coronaviruses, Zika virus, Lassa virus, Crimean Congo hemorrhagic fever virus, hepatitis E virus, and other RNA viruses. Also described herein are synthetic rocaglate compositions, uses thereof, and methods for reducing or inhibiting translation initiation of a messenger ribonucleic acid (mRNA) of a virus in a host cell or organism infected by the virus.

IPC Classes  ?

• A61K 31/665 - Phosphorus compounds having oxygen as a ring hetero atom, e.g. fosfomycin
• A61K 31/343 - Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin having five-membered rings with one oxygen as the only ring hetero atom, e.g. isosorbide condensed with a carbocyclic ring, e.g. coumaran, bufuralol, befunolol, clobenfurol, amiodarone
• A61P 31/14 - Antivirals for RNA viruses

Abstract

The present application relates to triterpene glycoside saponin-derived adjuvants, syntheses thereof, and intermediates thereto. The application also provides pharmaceutical compositions comprising compounds of the present invention and methods of using said compounds or compositions in the treatment of and immunization for infectious diseases.

IPC Classes  ?

• A61K 39/39 - Medicinal preparations containing antigens or antibodies characterised by the immunostimulating additives, e.g. chemical adjuvants
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• C07H 15/256 - Polyterpene radicals

Abstract

The present invention relates to methods and compositions for reducing the risk and severity of C. difficile infection. It is based, at least in part, on the discovery that a restricted fraction of the gut microbiota, including the bacterium Clostridium scindens, contributes substantially to resistance against C. difficile infection. Without being bound by any particular theory, it is believed that this is achieved through the biosynthesis of secondary bile acids.

IPC Classes  ?

• A61K 35/74 - Bacteria
• A61K 38/44 - Oxidoreductases (1)
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• C12N 9/04 - Oxidoreductases (1.), e.g. luciferase acting on CHOH groups as donors, e.g. glucose oxidase, lactate dehydrogenase (1.1)
• C12Q 1/26 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving oxidoreductase

Abstract

Provided herein are chimeric receptors, such as chimeric antigen receptors (CARs), comprising BCMA-binding molecules, such as anti-BCMA antibodies and antigen-binding fragments thereof, such as heavy chain variable (VH) regions and single-chain antibody fragments, and encoding polynucleotides. In some embodiments, the anti-BCMA chimeric receptors specifically bind to BCMA. Among the anti-BCMA-binding molecules are human antibodies, including those that compete for binding to BCMA with reference antibodies, such as a non-human reference antibody. Also provided are genetically engineered cells expressing the CARs and uses thereof such as in adoptive cell therapy.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 38/00 - Medicinal preparations containing peptides
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12Q 1/6848 - Nucleic acid amplification reactions characterised by the means for preventing contamination or increasing the specificity or sensitivity of an amplification reaction

Abstract

Provided herein are compositions and methods for adoptive cell therapy comprising engineered immune cells that express a tumor antigen-targeted chimeric antigen receptor and a prodrug converting enzyme.

IPC Classes  ?

• C07D 239/94 - Nitrogen atoms
• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• C07C 235/34 - Carboxylic acid amides, the carbon skeleton of the acid part being further substituted by oxygen atoms having carbon atoms of carboxamide groups bound to acyclic carbon atoms and singly-bound oxygen atoms bound to the same carbon skeleton the carbon skeleton containing six-membered aromatic rings having the nitrogen atoms of the carboxamide groups bound to hydrogen atoms or to acyclic carbon atoms
• C07H 19/16 - Purine radicals
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C12N 5/078 - Cells from blood or from the immune system
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 9/48 - Hydrolases (3.) acting on peptide bonds, e.g. thromboplastin, leucine aminopeptidase (3.4)

Abstract

Described herein are multi-specific binding agents that bind A33 and 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid. Also provided herein are methods of using multi-specific binding agents or compositions thereof for the detection, prevention, and/or therapeutic treatment of diseases characterized by expression of the A33 glycoprotein antigen, in particular, colorectal cancer.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 51/04 - Organic compounds
• A61P 35/00 - Antineoplastic agents

Abstract

Provided herein are compositions and methods for adoptive cell therapy comprising engineered immune cells that express a tumor antigen-targeted chimeric antigen receptor and a prodrug converting enzyme.

IPC Classes  ?

• C12N 9/48 - Hydrolases (3.) acting on peptide bonds, e.g. thromboplastin, leucine aminopeptidase (3.4)
• A61K 9/00 - Medicinal preparations characterised by special physical form
• A61K 38/00 - Medicinal preparations containing peptides
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/00 - Antineoplastic agents
• C07D 239/94 - Nitrogen atoms
• C07D 501/16 - Compounds having a nitrogen atom directly attached in position 7 with a double bond between positions 2 and 3
• C07D 519/00 - Heterocyclic compounds containing more than one system of two or more relevant hetero rings condensed among themselves or condensed with a common carbocyclic ring system not provided for in groups or
• C07K 14/52 - CytokinesLymphokinesInterferons
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

in vivoin vivo mRNA and/or polypeptide levels of proinflammatory cytokines (e.g. interleukin-6, IFNy, IL-1, IL-10, TNFα, etc.) and methods of using the same to monitor adverse CAR-T-related toxicity in cancer patients.

IPC Classes  ?

• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G01N 27/02 - Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
• A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
• A61B 5/0538 - Measuring electrical impedance or conductance of a portion of the body invasively, e.g. using a catheter

Abstract

Provided herein are compositions, kits, and methods for manufacturing cells for adoptive cell therapy comprising engineered immune cells that express express a PABPC1- or PABPC4-specific receptor (e.g., a PABPC1- or PABPC4-specific chimeric antigen receptor), and uses thereof.

IPC Classes  ?

• C07K 14/725 - T-cell receptors
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Goods & Services

Investment services, namely, venture capital financing, venture capital investment, and funding services for companies in the healthcare, medical, life sciences, biotechnology, and digital health fields

Abstract

Presented herein are systems and methods for generating data structures for data structures for events detected across data sources in network environments. A computing system may maintain, on a data repository, a profile for a subject at risk of or diagnosed with cancer. Presented herein are systems and methods for generating data structures for data structures for events detected across data sources in network environments. A computing system may maintain, on a data repository, a profile for a subject at risk of or diagnosed with cancer. The profile may identify a plurality of event identifiers for a corresponding plurality of events associated with administration of radiotherapy to the subject. The computing system may apply a prompt based on a request and at least a portion of the profile to a generative machine learning (ML) model. The computing system may generate, based on applying the prompt to the generative ML model, a data structure comprising (i) a plurality of nodes corresponding to the respective plurality of event identifiers and (ii) a plurality of edges each defining a relationship between a corresponding pair of the plurality of nodes.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06N 3/0475 - Generative networks
• G06T 7/00 - Image analysis
• G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Abstract

Provided herein are compositions, kits, and methods for manufacturing cells for adoptive cell therapy comprising engineered immune cells that express express a U5 snRNP200-specific receptor (e.g., a U5 snRNP200-specific chimeric antigen receptor), and uses thereof.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 15/85 - Vectors or expression systems specially adapted for eukaryotic hosts for animal cells
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61P 35/00 - Antineoplastic agents

Abstract

Presented herein are systems and methods of classifying samples based on flow cytometry data. A computing system may receive a dataset comprising a plurality of event sequences for a plurality of flow channels. The flow channels may correspond to white blood cell types in a sample obtained from a subject at risk of or diagnosed with cancer. The computing system may apply the dataset to a first machine learning (ML) model to determine a plurality of first values for the plurality of event sequences. The computing system may sort the dataset by the plurality of first values. The computing system may apply the sorted dataset to a second ML model to determine a second value indicating a probability of tumor associated with cancer in the sample of the subject. The computing system may generate a classification in accordance with the second value.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G01N 15/1429 - Signal processing
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G06N 3/045 - Combinations of networks

Abstract

Provided herein are compositions, kits, and methods for manufacturing cells for adoptive cell therapy comprising engineered immune cells that express single-chain T cell receptor-chimeric antigen receptors (scTCR-CARs), and uses thereof.

IPC Classes  ?

• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C12N 15/62 - DNA sequences coding for fusion proteins
• C12N 15/86 - Viral vectors
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/32 - T-cell receptors [TCR]
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells

Abstract

The present disclosure is directed to systems and methods that may receive an image, wherein the image includes an annotation at least partially enclosing a region of interest (“ROI”), wherein the image has a plurality of pixels. The systems and methods may use a first algorithm to determine at least one foreground and at least one background from the image. The systems and methods may use a second algorithm to determine a plurality of annotation pixels from the plurality of pixels of the image. The systems and methods may intersect outputs from the first algorithm and the second algorithm to determine an intersection which defines the ROI.

IPC Classes  ?

• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 5/70 - DenoisingSmoothing
• G06T 7/00 - Image analysis
• G06T 7/194 - SegmentationEdge detection involving foreground-background segmentation
• G06V 30/14 - Image acquisition
• G06V 30/18 - Extraction of features or characteristics of the image
• G06V 30/19 - Recognition using electronic means

Abstract

The present technology relates generally to carbon nanotube antibody fragment conjugates useful for the detection of a bioanalyte, and methods of making and using the same. In particular, a conjugate for detection of a bioanalyte includes a single-walled carbon nanotube with a sp3 quantum-well defect site (QWNT) conjugated to an antibody fragment via an amide bond at the sp3 quantum-well defect site; wherein the antibody fragment is a single-chain variable fragment with an overall net charge of about -10 to about -30 at a pH of about 6.5 to about 7.5, the antibody fragment changes phase in response to interaction with the bioanalyte, and the antibody fragment comprises Vu-linker-Vr, wherein VH is a variable heavy chain domain, Vi. is a variable light chain domain; and wherein the antibody fragment comprises a number of D residues to provide the overall net charge.

IPC Classes  ?

• G01N 33/563 - ImmunoassayBiospecific binding assayMaterials therefor involving antibody fragments
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

Presented herein are systems and methods for classifying biomedical images for executing operations. A computing system may identify a biomedical image of a slide with a biological sample obtained from a subject at risk of a condition; apply the biomedical image to a machine learning model; generate, based on applying the biomedical image to the ML model, a classification corresponding to the biomarker associated with the condition in biological sample on the slide; and execute an operation with respect to the slide for testing of the biological sample, in accordance with the classification for the biomedical image.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G06F 18/241 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
• G06N 20/00 - Machine learning
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Described embodiments provide systems and methods for generating a corrected planar scintigraphy image (CPSI) corrected for image artifacts. A computing system can obtain a plurality of planar scintigraphy images of a subject. The plurality of planar scintigraphy images may contain image artifacts caused by one or more physical processes. The computing system may generate a corrected CPSI by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images. The planar scintigraphy image reconstruction model may comprise a first non-negativity constraint and a second non-negativity constraint, and be based on a first regularization term, a second regularization term, a coupling term and a fidelity term. The computing system may present the CPSI for evaluation of a condition of the subject. Presenting the CPSI may comprise at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation

Abstract

Provided herein are compositions, methods, and uses involving bispecific binding molecules that specifically bind to HER2, a receptor tyrosine kinase, and to CD3, a T cell receptor, and mediate T cell cytotoxicity for managing and treating disorders, such as cancer. Also provided herein are uses and methods for managing and treating HER2-related cancers.

IPC Classes  ?

• C07K 16/32 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against translation products from oncogenes
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/36 - Immune checkpoint inhibitors
• A61K 40/42 - Cancer antigens
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/46 - Hybrid immunoglobulins

Goods & Services

Providing scientific research and analysis services in the field of immunology, namely, proprietary methods for analyzing T cell receptor sequencing data for scientific and medical research and health treatment purposes; providing software for analyzing T cell receptor sequences and identifying T cell clonal lineages; bioinformatics services for identifying and tracking T cell populations in connection with monitoring immune response dynamics, endogenous and with therapeutic interventions.

Abstract

Provided herein are compositions, methods, and uses involving bispecific binding molecules that specifically bind to HER2, a receptor tyrosine kinase, and to CD3, a T cell receptor, and mediate T cell cytotoxicity for managing and treating disorders, such as cancer. Also provided herein are uses and methods for managing and treating HER2-related cancers.

IPC Classes  ?

• C07K 16/46 - Hybrid immunoglobulins
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 40/10 - Cellular immunotherapy characterised by the cell type used
• A61K 40/42 - Cancer antigens
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/00 - Antineoplastic agents
• A61P 35/02 - Antineoplastic agents specific for leukemia
• A61P 35/04 - Antineoplastic agents specific for metastasis
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/32 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against translation products from oncogenes

Abstract

The present invention provides various compositions and methods useful for the treatment of cancer, such as cancers that are resistant to immune checkpoint blockade and/or are resistant to treatment with PD-1, PD-L1 or CTLA-4 inhibitors. In some embodiments the present invention provides compositions comprising one or more CD40 agonists (e.g. CD40 agonist antibodies), TLR agonists, and/or IL10 receptor inhibitors or IL10 inhibitors, and/or various combinations thereof, optionally together with one or more immune checkpoint inhibitors, and the use of such compositions in treatment of tumors.

IPC Classes  ?

• H04L 51/216 - Handling conversation history, e.g. grouping of messages in sessions or threads
• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06N 3/09 - Supervised learning
• H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• H04L 51/58 - Message adaptation for wireless communication
• H04M 1/72436 - User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages for text messaging, e.g. short messaging services [SMS] or e-mails

Abstract

Described herein are compounds of Formula (I), pharmaceutical compositions comprising the compounds, and methods of using the compounds for treating cancer and for inhibiting the autophagy-lysosomal pathway (ALP).

IPC Classes  ?

• A61K 31/395 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
• A61K 31/47 - QuinolinesIsoquinolines
• A61K 31/13 - Amines, e.g. amantadine
• A61K 31/33 - Heterocyclic compounds

IPC Classes  ?

• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/42 - Cancer antigens
• A61P 37/06 - Immunosuppressants, e.g. drugs for graft rejection
• C12N 15/12 - Genes encoding animal proteins

IPC Classes  ?

• A61K 31/43 - Compounds containing 4-thia-1-azabicyclo [3.2.0] heptane ring systems, i.e. compounds containing a ring system of the formula , e.g. penicillins, penems
• A61K 38/14 - Peptides containing saccharide radicalsDerivatives thereof
• A61P 31/04 - Antibacterial agents
• C12Q 1/04 - Determining presence or kind of microorganismUse of selective media for testing antibiotics or bacteriocidesCompositions containing a chemical indicator therefor

IPC Classes  ?

• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61P 35/00 - Antineoplastic agents
• C07K 7/06 - Linear peptides containing only normal peptide links having 5 to 11 amino acids
• C07K 7/08 - Linear peptides containing only normal peptide links having 12 to 20 amino acids
• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals

IPC Classes  ?

• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61P 35/00 - Antineoplastic agents
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 19/00 - Hybrid peptides
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells
• C12N 15/63 - Introduction of foreign genetic material using vectorsVectorsUse of hosts thereforRegulation of expression

Abstract

Systems and methods for fast reconstruction of motion-resolved magnetic resonance images using space-time-coil convolutional networks are disclosed. The system can receive a plurality of k-space data sets. The system can detect a motion signal therefrom. The system can classify the k-space data sets according to states of the motion signals. The system can resolve the k-space data set to Euclidean space images. The system can resolve the Euclidean space images to a combined Euclidian space image. For example, the system can use a convolutional network that exploits spatial, temporal and coil correlations without k-space data consistency to minimize computation time.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation
• G06N 3/0464 - Convolutional networks [CNN, ConvNet]

Abstract

The present disclosure, in certain embodiments, provides methods for generating midbrain dopamine neurons and precursors thereof, midbrain dopamine neurons and precursors thereof generated by such methods and compositions comprising such cells, and uses thereof for preventing, modeling, and/or treating a neurological disorder.

IPC Classes  ?

• A61K 35/30 - NervesBrainEyesCorneal cellsCerebrospinal fluidNeuronal stem cellsNeuronal precursor cellsGlial cellsOligodendrocytesSchwann cellsAstrogliaAstrocytesChoroid plexusSpinal cord tissue
• A61K 35/545 - Embryonic stem cellsPluripotent stem cellsInduced pluripotent stem cellsUncharacterised stem cells
• C12N 5/0793 - Neurons
• A61K 35/12 - Materials from mammalsCompositions comprising non-specified tissues or cellsCompositions comprising non-embryonic stem cellsGenetically modified cells
• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals

Abstract

The present disclosure relates generally to immunoglobulin-related compositions (e.g., antibodies or antigen binding fragments thereof) that can bind to the CD24 protein. The antibodies of the present technology are useful in methods for detecting and treating a CD24-associated cancer in a subject in need thereof.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61P 35/00 - Antineoplastic agents

Abstract

The present disclosure provides compositions and methods for identifying the functional effects/phenotypes associated with RNA binding protein (RBP)-RNA interactions (PRIs).

IPC Classes  ?

• C12N 9/22 - Ribonucleases
• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• C12N 15/52 - Genes encoding for enzymes or proenzymes
• C12N 15/62 - DNA sequences coding for fusion proteins
• C12N 15/63 - Introduction of foreign genetic material using vectorsVectorsUse of hosts thereforRegulation of expression
• C12N 15/90 - Stable introduction of foreign DNA into chromosome
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells
• C12N 1/21 - BacteriaCulture media therefor modified by introduction of foreign genetic material

Abstract

The presently disclosed subject matter provides methods and compositions for enhancing immune responses toward tumor and pathogen antigens. It relates to fusion polypeptide that can be expressed in cells (e.g., immunoresponsive cells comprising an antigen-recognizing receptor) to improve the activity and/or efficiency of the cells. In certain embodiments, the fusion polypeptide comprises an extracellular domain comprising an antigen-binding fragment and a co-stimulatory ligand polypeptide, and an intracellular domain comprising a first co-stimulatory molecule polypeptide.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 15/85 - Vectors or expression systems specially adapted for eukaryotic hosts for animal cells

Abstract

The presently disclosed subject matter provides cells, compositions, and methods for inducing B cell depletion. It relates to cells comprising an antigen-recognizing receptor (e.g., a chimeric antigen receptor (CAR)) targeting a B cell antigen (e.g., CD19, FcRLS, etc.).

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 35/15 - Cells of the myeloid line, e.g. granulocytes, basophils, eosinophils, neutrophils, leucocytes, monocytes, macrophages or mast cellsMyeloid precursor cellsAntigen-presenting cells, e.g. dendritic cells
• A61P 35/00 - Antineoplastic agents
• A61P 37/06 - Immunosuppressants, e.g. drugs for graft rejection

Abstract

Presented herein are systems and methods of determining values indicating degrees of risk for venous thromboses (VTEs) in subjects. A computing system can identify a first plurality of images for a first subject at risk of VTE, each of the first plurality of images corresponding to a respective white blood cell (WBC) in a first blood sample obtained from the first subject at a first time. The computing system can provide the first plurality of images to a machine learning (ML) architecture. The computing system can generate a plurality of embedding sets, each embedding set of the plurality of embedding sets corresponding to a respective image of the first plurality of images. The computing system can determine, based on executing the ML architecture, a value indicating a degree of risk of VTE for the first subject at the time interval relative to the first time.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G06N 20/20 - Ensemble learning
• A61K 31/727 - HeparinHeparan
• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients

Abstract

The presently disclosed subject matter provides antibodies that bind to GPRC5D and methods of using the same.

IPC Classes  ?

• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61P 35/00 - Antineoplastic agents
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/46 - Hybrid immunoglobulins
• C07K 19/00 - Hybrid peptides
• C12N 15/13 - Immunoglobulins
• C12N 15/63 - Introduction of foreign genetic material using vectorsVectorsUse of hosts thereforRegulation of expression

Abstract

Provided herein are compositions, kits, and methods for manufacturing cells for treating or regenerating damaged or diseased tissue comprising engineered fibroblasts, myoblasts, osteocytes, chondrocytes, adipocytes, epithelial cells, or mesenchymal stem cells that overexpress glucose transporter 5 (GLUTS).

IPC Classes  ?

• C12N 5/071 - Vertebrate cells or tissues, e.g. human cells or tissues
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells
• A61P 21/00 - Drugs for disorders of the muscular or neuromuscular system
• A61K 48/00 - Medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseasesGene therapy
• A61P 17/02 - Drugs for dermatological disorders for treating wounds, ulcers, burns, scars, keloids, or the like
• A61K 31/409 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with one nitrogen as the only ring hetero atom, e.g. sulpiride, succinimide, tolmetin, buflomedil having four such rings, e.g. porphine derivatives, bilirubin, biliverdine
• A61K 35/33 - Fibroblasts

Abstract

Provided herein are compounds that promote targeted degradation of IKZF1, IKZF2, GSPT1, and/or CK1a, proteins whose activities are implicated in the pathology of certain cancers (e.g., acute myeloid leukemia). Also provided are pharmaceutical compositions comprising the compounds. Also provided are methods of treating cancer, and methods of promoting the degradation of IKZF1, IKZF2, GSPT1, and/or CK1a in a subject or biological sample by administering a compound or composition described herein.

IPC Classes  ?

• A61K 31/538 - 1,4-Oxazines, e.g. morpholine ortho- or peri-condensed with carbocyclic ring systems
• A61K 31/454 - Non-condensed piperidines, e.g. piperocaine containing further heterocyclic ring systems containing a five-membered ring with nitrogen as a ring hetero atom, e.g. pimozide, domperidone
• A61K 31/4709 - Non-condensed quinolines containing further heterocyclic rings
• A61P 35/02 - Antineoplastic agents specific for leukemia
• C07D 401/04 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing two hetero rings directly linked by a ring-member-to-ring- member bond
• C07D 401/14 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, at least one ring being a six-membered ring with only one nitrogen atom containing three or more hetero rings
• C07D 405/14 - Heterocyclic compounds containing both one or more hetero rings having oxygen atoms as the only ring hetero atoms, and one or more rings having nitrogen as the only ring hetero atom containing three or more hetero rings
• C07D 409/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having sulfur atoms as the only ring hetero atoms containing three or more hetero rings
• C07D 413/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and oxygen atoms as the only ring hetero atoms containing three or more hetero rings
• C07D 417/14 - Heterocyclic compounds containing two or more hetero rings, at least one ring having nitrogen and sulfur atoms as the only ring hetero atoms, not provided for by group containing three or more hetero rings

Abstract

Presented herein are systems and methods of reconstructing tomograms from projection datasets obtained during radiotherapy. A computing system may receive a projection dataset corresponding to a portion of an arc about a first structure of interest (SOI) in a first volume of a subject acquired in accordance with a cone beam computed tomography (CBCT). The computing system may generate, based on applying the projection dataset to a machine learning (ML) model, a tomogram (e.g., a perfusion image)corresponding to the first volume of the subject. The computing system may store, using one or more data structures, an association between the subject and the tomogram.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation
• A61B 6/03 - Computed tomography [CT]
• A61B 6/40 - Arrangements for generating radiation specially adapted for radiation diagnosis
• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06N 20/00 - Machine learning

Abstract

Systems and methods are provided for optimizing a radiotherapy treatment for a patient. A method for optimizing radiotherapy treatment comprises discretizing a region of interest of the patient into a plurality of voxels, wherein the voxels comprise tumor voxels and non-tumor voxels; receiving a prescribed radiation dose for each tumor voxel; discretizing one or more radiation emitters of a radiotherapy machine into a plurality of two- dimensional beamlets; defining an initial intensity vector for each beamlet; determining a radiation dose delivered to each voxel by each 2D beamlet, the radiation dose based on the initial intensity vector, thereby creating a dose influence matrix; compressing the dose influence matrix; applying an automated planning algorithm to approximate the prescribed radiation dose for tumor voxels and minimize the radiation dosage for non-tumor voxels, thereby creating an optimized intensity vector; and providing an optimized radiotherapy treatment for the patient based on the optimized intensity vector.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present disclosure relates generally to immunoglobulin-related compositions (e.g., antibodies or antigen binding fragments thereof) that can bind to and neutralize the activity of polysialic acid. The antibodies of the present technology are useful in methods for detecting and treating a polysialic acid-associated cancer in a subject in need thereof.

IPC Classes  ?

• C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
• A61P 35/00 - Antineoplastic agents

Abstract

The presently disclosed subject matter provides cells, compositions and methods for enhancing immune responses toward tumor antigens. It relates to cells comprising an antigen-recognizing receptor (e.g., a HIT receptor) and an inhibitory polynucleotide. These cells have improved activity and/or efficiency.

IPC Classes  ?

• A61K 40/32 - T-cell receptors [TCR]
• A61K 9/51 - Nanocapsules
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• C12N 15/867 - Retroviral vectors
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells
• C12N 15/62 - DNA sequences coding for fusion proteins
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61P 35/00 - Antineoplastic agents

Abstract

The presently disclosed subject matter provides compositions and methods for targeting immune responses toward tumor antigen-bearing cells. It relates to cells, e.g., modified immunoresponsive cells, comprising an antigen-recognizing receptor (e.g., a chimeric antigen receptor (CAR)) and an immunoevasins (e.g., a NEF polypeptide).

IPC Classes  ?

• A61K 40/46 - Viral antigens
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/15 - Natural-killer [NK] cellsNatural-killer T [NKT] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/50 - Cellular immunotherapy characterised by the use of allogeneic cells
• C07K 14/005 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from viruses
• C07K 14/725 - T-cell receptors

Abstract

Disclosed are methods for inhibiting proliferation of or inducing apoptosis in certain leukemia cells or both. The methods comprise contacting a leukemia cell exhibiting an NPM1 mutation with a pharmacologic inhibitor of interaction between MLL and menin. More broadly, disclosed are methods for treating a susceptible leukemia using pharmacologic inhibition of Menin-MLL interaction. Also disclosed are methods for treating such leukemias using inhibition of Menin-MLL interaction in combination with DOT1L inhibition.

IPC Classes  ?

• A61K 31/519 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim ortho- or peri-condensed with heterocyclic rings
• A61K 31/7064 - Compounds having saccharide radicals and heterocyclic rings having nitrogen as a ring hetero atom, e.g. nucleosides, nucleotides containing six-membered rings with nitrogen as a ring hetero atom containing condensed or non-condensed pyrimidines
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61P 35/02 - Antineoplastic agents specific for leukemia

Abstract

The presently disclosed subject matter provides methods and compositions for treating myeloid disorders (e.g., acute myeloid leukemia (AML)). It relates to immunoresponsive cells bearing antigen recognizing receptors (e.g., chimeric antigen receptors (CARs)) targeting AML-specific antigens.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61K 48/00 - Medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseasesGene therapy
• A61P 35/00 - Antineoplastic agents
• A61P 35/02 - Antineoplastic agents specific for leukemia
• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/715 - ReceptorsCell surface antigensCell surface determinants for cytokinesReceptorsCell surface antigensCell surface determinants for lymphokinesReceptorsCell surface antigensCell surface determinants for interferons
• C07K 14/72 - ReceptorsCell surface antigensCell surface determinants for hormones
• C07K 16/00 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• G01N 33/566 - ImmunoassayBiospecific binding assayMaterials therefor using specific carrier or receptor proteins as ligand binding reagent
• G01N 33/569 - ImmunoassayBiospecific binding assayMaterials therefor for microorganisms, e.g. protozoa, bacteria, viruses
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
• G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16B 20/30 - Detection of binding sites or motifs
• G16B 20/50 - Mutagenesis
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids

Abstract

The present disclosure relates to novel lantibiotics, lantibiotic pharmaceutical compositions, isolated and recombinant lantibiotic-producing bacteria, bacterial pharmaceutical compositions, methods of producing novel lantibiotics from lantibiotic-producing bacteria, and methods of using such lantibiotics, lantibiotic pharmaceutical compositions, and bacterial pharmaceutical compositions to treat gram-positive bacteria infections, including vancomycin resistant enterococci infections, in patients, and to treat food and other objects to avoid gram-positive bacteria contamination.

IPC Classes  ?

• A61K 38/12 - Cyclic peptides
• A61K 38/44 - Oxidoreductases (1)
• A61P 31/04 - Antibacterial agents
• C12N 1/20 - BacteriaCulture media therefor
• C12P 21/00 - Preparation of peptides or proteins

Abstract

Presented herein are systems and methods for determining quality of brachytherapy treatment plans from catheter placement in organs in subjects. A computing system can identify a biomedical image of a section associated with an organ of a subject to be administered with brachytherapy in accordance with a treatment plan. The computing system can detect, from the biomedical image, (i) a plurality of positions corresponding to a plurality of catheters placed within the organ and (ii) a contour defining a structure of interest (SOI) in the biomedical image corresponding to a feature within the organ. The computing system can provide the plurality of positions and the contour as input to a machine learning (ML) model. The computing system can generate, based on providing the input to the ML model, a metric indicating a quality of the treatment plan for the plurality of catheters placed relative to the feature within the organ.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06N 20/00 - Machine learning
• G06T 7/00 - Image analysis
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• A61B 34/10 - Computer-aided planning, simulation or modelling of surgical operations
• A61B 34/20 - Surgical navigation systemsDevices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis

Abstract

The present disclosure relates to compounds according to Formula I or a pharmaceutically acceptable salt and/or solvate thereof, as well as compositions including such compounds and uses thereof, where R1is alkyl, cycloalkyl, cycloalkenyl, heterocyclyl, aryl, or heteroaryl; R2is H, alkyl, halo, amino, amide, hydroxyl, or O-R5; R3is alkyl, cycloalkyl, heterocyclyl, aryl, or heteroaryl; R4is H or alkyl; R5is alkyl, cycloalkyl, cycloalkenyl, heterocyclyl, aryl, or heteroaryl; Z1is CH, N, or C-R6; Z2is CH, N, or C-R7; Z3is CH, N, or C-R8; and R6, R7, and R8 are each independently alkyl, cycloalkyl, halo, amino, amide, hydroxy, or alkoxy. As evidenced by this application, these compounds and compositions are suitable for, among other things, treating cancer.

IPC Classes  ?

• A61K 31/395 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
• A61K 31/13 - Amines, e.g. amantadine
• A61K 31/44 - Non-condensed pyridinesHydrogenated derivatives thereof
• A61K 31/33 - Heterocyclic compounds

Abstract

Presented herein are systems and methods for determining scores from biomedical images. A computing system may identify a plurality of tiles in a first biomedical image derived from a sample of a subject. Each tile may correspond to features of the sample. The computing system may apply the plurality of tiles to a machine learning (ML) model. The ML model may include: an encoder to generate a plurality of feature vectors based on the plurality of tiles; a clusterer to select a subset from the plurality of feature vectors; and an aggregator to determine a first score indicative of a time to an event for the subject resulting from the features of the sample. The model may be trained in accordance with a loss derived from second scores determined for second biomedical images. The computing system may store an association between the score and the first biomedical image.

IPC Classes  ?

• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects

Abstract

Presented herein are systems and methods relating to artificial intelligence-drive intraoperative diagnosis. For example, a method can include capturing, by an optical reader device of a mobile device, an image of a tissue. A method can further include providing, by a mobile application of the mobile device, the image of the tissue to a tissue analysis circuit. A method can include receiving, from the tissue analysis circuit via the mobile device, a tissue classification. A method can include presenting, via a graphical user interface of the mobile device, a display screen comprising the tissue classification.

IPC Classes  ?

• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• G06T 7/00 - Image analysis
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Goods & Services

Downloadable computer software and computer algorithms for analyzing next-generation sequencing data to identify and predict tumor-specific neoantigens for use in scientific research and medical research in the fields of oncology, immunology, and precision medicine Non-downloadable computer software and computer algorithms for analyzing next-generation sequencing data to identify and predict tumor-specific neoantigens for use in scientific research and medical research in the fields of oncology, immunology, and precision medicine; providing scientific research and analysis services in the field of immunology, namely, proprietary methods for analyzing T cell receptor sequencing data for scientific and medical research and health treatment purposes; providing software for analyzing T cell receptor sequences and identifying T cell clonal lineages; bioinformatics services for identifying and tracking T cell populations in connection with monitoring immune response dynamics, endogenous and with therapeutic interventions; research in the biomedical sciences fields, including molecular biology, cell biology, genetics, and immunology; Medical initiative services, namely, research and development services pertaining to the development of cancer vaccines and treatment Medical services; Healthcare services for treating cancer; Hospital services, namely, patient treatment and care; Pharmaceutical prescription formulation being compounding, and dispensing services; Providing medical services in the nature of patient consultations and medical information via telemedicine

Abstract

The present disclosure provides methods for treating leukemia (e.g., AML) using a CLK/DYRK inhibitor and a B-cell lymphoma 2 (BCL2) inhibitor (e.g., venetoclax). Kits for use in practicing the methods are also provided.

IPC Classes  ?

• A61K 31/496 - Non-condensed piperazines containing further heterocyclic rings, e.g. rifampin, thiothixene or sparfloxacin
• A61K 31/404 - Indoles, e.g. pindolol
• A61K 31/635 - Compounds containing para-N-benzene- sulfonyl-N-groups, e.g. sulfanilamide, p-nitrobenzenesulfonohydrazide having a heterocyclic ring, e.g. sulfadiazine
• A61P 35/02 - Antineoplastic agents specific for leukemia

Abstract

The presently disclosed subject matter provides for in vitro methods of inducing differentiation of stem cells (e.g., human stem cells) into somatotrophs, and differentiated cells generated by such methods. The presently disclosed subject matter also provides for uses of such cells for treating growth hormone deficiency.

IPC Classes  ?

• C12N 5/071 - Vertebrate cells or tissues, e.g. human cells or tissues
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing

Abstract

The presently disclosed subject matter provides antibodies or antigen-binding fragments thereof that bind to mesothelin and methods of using such antibodies or antigen-binding fragments thereof. The presently disclosed subject further provides immunoconjugates comprising anti-mesothelin antibodies or antigen-binding fragments thereof and methods of using such immunoconjugates.

IPC Classes  ?

• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
• A61P 35/00 - Antineoplastic agents

Abstract

The presently disclosed subject matter provides methods for treating neoplasia using cells comprising an antigen-recognizing receptor (e.g., a chimeric antigen receptor (CAR)) that specifically targets mesothelin.

IPC Classes  ?

• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 9/127 - Synthetic bilayered vehicles, e.g. liposomes or liposomes with cholesterol as the only non-phosphatidyl surfactant
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C12N 15/86 - Viral vectors
• C12N 5/0735 - Embryonic stem cellsEmbryonic germ cells
• C12N 5/074 - Adult stem cells
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 5/10 - Cells modified by introduction of foreign genetic material, e.g. virus-transformed cells
• C12N 15/62 - DNA sequences coding for fusion proteins
• A61P 35/00 - Antineoplastic agents

Abstract

Presented herein are systems and methods for detecting basal-cell carcinoma (BCC) in biomedical images of skin lesions. A computing system may identify, for a first subject at risk of BCC in a first lesion on a region of an epidermis: (i) a first biomedical image of an outer layer of the region on the epidermis of the first subject acquired in accordance with dermoscopy, and (ii) a plurality of second biomedical images of at least one inner layer in the region on the epidermis of the first subject acquired in accordance with reflection confocal microscopy (RCM). The computing system may apply the first biomedical image and the plurality of second biomedical images to a model architecture. The computing system may generate, based on applying to the model architecture, a score indicating a likelihood of BCC in the first lesion.

IPC Classes  ?

• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G06N 3/08 - Learning methods
• A61P 17/02 - Drugs for dermatological disorders for treating wounds, ulcers, burns, scars, keloids, or the like
• G16H 30/00 - ICT specially adapted for the handling or processing of medical images

Abstract

Presented herein are systems and methods of determining tumor responses in brains from administering radiotherapy. A computing system can: identify a plurality of biomedical images of a brain of a subject; perform an image registration on a first biomedical image with a second biomedical image to determine a plurality of translation parameters; generate a third biomedical image using the second biomedical image in accordance with the plurality of translation parameters, detect using an image segmentation model, (i) a first segment identifying a first region within the third biomedical image and (ii) a second segment identifying a second region within the second biomedical image; and determine a metric indicating a degree of responsiveness of a tumor in the subject to the administration of the radiotherapy to the brain, based on the first segment and the second segment.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 7/11 - Region-based segmentation
• G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
• G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

The present disclosure provides methods and systems for treating tumors with radiotherapy, wherein a first dose of radiation is administered to a first sub-volume of the tumor and a second dose of radiation is administered to a second sub-volume of the tumor.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• A61K 41/00 - Medicinal preparations obtained by treating materials with wave energy or particle radiation

Abstract

The present technology relates generally to compounds for inhibiting M24B aminopeptidases to selectively activating the CARDS inflammasome, an innate immune signaling platform that triggers pyroptotic cell death.

IPC Classes  ?

• C07D 403/06 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a carbon chain containing only aliphatic carbon atoms
• C07C 237/12 - Carboxylic acid amides, the carbon skeleton of the acid part being further substituted by amino groups having the carbon atoms of the carboxamide groups bound to acyclic carbon atoms of the carbon skeleton the carbon skeleton being acyclic and saturated having the nitrogen atom of at least one of the carboxamide groups bound to an acyclic carbon atom of a hydrocarbon radical substituted by carboxyl groups
• C07D 207/06 - Heterocyclic compounds containing five-membered rings not condensed with other rings, with one nitrogen atom as the only ring hetero atom with only hydrogen or carbon atoms directly attached to the ring nitrogen atom having no double bonds between ring members or between ring members and non-ring members with radicals, containing only hydrogen and carbon atoms, attached to ring carbon atoms
• C07D 207/16 - Carbon atoms having three bonds to hetero atoms with at the most one bond to halogen, e.g. ester or nitrile radicals
• C07D 403/04 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings directly linked by a ring-member-to-ring- member bond
• C07D 403/12 - Heterocyclic compounds containing two or more hetero rings, having nitrogen atoms as the only ring hetero atoms, not provided for by group containing two hetero rings linked by a chain containing hetero atoms as chain links

Abstract

Presented herein are systems and methods for identifying targets in x-ray projection images. A computing system can obtain a first dataset for a first subject. The first dataset can include (i) a first projection image acquired via a first x-ray scan of a first volume having a first structure of interest (SOI) moving within the first subject and (ii) first information to be used to define motion of the first SOI in the first subject. The computing system can apply a machine learning (ML) model to the first dataset. The ML model may be established for at least the first subject using training data. The computing system can identify, based on applying the ML model, a target defining the first SOI within the first projection image. The computing system can store, using one or more data structures, an association between the first subject and the target.

IPC Classes  ?

• A61N 5/10 - X-ray therapyGamma-ray therapyParticle-irradiation therapy
• G06T 11/00 - 2D [Two Dimensional] image generation
• G06T 7/00 - Image analysis
• A61N 5/06 - Radiation therapy using light

Abstract

The instant disclosure provides antibodies that specifically bind to CTLA-4 (e.g., human CTLA-4) and antagonize CTLA-4 function. Also provided are pharmaceutical compositions comprising these antibodies, nucleic acids encoding these antibodies, expression vectors and host cells for making these antibodies, and methods of treating a subject using these antibodies.

IPC Classes  ?

• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61P 11/00 - Drugs for disorders of the respiratory system
• A61P 35/04 - Antineoplastic agents specific for metastasis
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants

Abstract

Described herein are Deep Multi-Magnification Networks (DMMNs). The method identifies, by a computing system, for a first tile of a biomedical image, the first tile comprising a portion of the biomedical image, a first patch associated with the first tile at a first magnification factor and a second patch associated with the first tile at a second magnification factor; applies, by the computing system, the first patch and the second patch to a machine learning (ML) model, the ML model comprising: a first network to generate a first feature map using the first patch, and a second network to generate a second feature map using the second patch; and determine a combination of the first feature map and the second feature map. Additionally, a computing system having one or more processors coupled with memory, configured to execute the method.

IPC Classes  ?

• G06T 7/11 - Region-based segmentation
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/045 - Combinations of networks
• G06T 7/00 - Image analysis
• G06T 19/00 - Manipulating 3D models or images for computer graphics
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]

Abstract

Provided herein are compositions, methods, and uses involving anti-Mucin-16 (MUC16) agents that immunospecifically bind an epitope of Mucin-16 (MUC16). Also provided herein are uses and methods for managing, treating, or preventing disorders, such as cancer and diseases associated with positive MUC16 expression.

IPC Classes  ?

• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61P 35/00 - Antineoplastic agents

Abstract

Compounds are described with the general formula Compounds are described with the general formula Compounds are described with the general formula wherein R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, and n are defined as anywhere herein, which are useful for the treatment of cancer and other dysproliferative diseases.

IPC Classes  ?

• C07D 307/93 - Heterocyclic compounds containing five-membered rings having one oxygen atom as the only ring hetero atom ortho- or peri-condensed with carbocyclic rings or ring systems condensed with a ring other than six-membered
• A61P 35/00 - Antineoplastic agents

Abstract

Provided herein are compositions, methods, and uses involving antibodies that immunospecifically bind the Galactin-3 (LGALS3) carbohydrate binding domain (CBD). Also provided herein are uses and methods for managing, treating, or preventing disorders, such as cancer.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 39/00 - Medicinal preparations containing antigens or antibodies
• A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
• A61P 35/04 - Antineoplastic agents specific for metastasis
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants

Abstract

Presented herein are systems and methods of selecting a cancer therapy in a cancer subject. A computing system may obtain, for a subject at risk of or diagnosed with cancer, a dataset comprising genome sequence reads of a biological sample obtained from the cancer subject. The computing system may identify, from the dataset, a first portion of the genome sequence reads corresponding to candidate Escherichia reads. The computing system may filter reads corresponding to a bacteriophage from the first portion of the genome sequence reads to generate a second portion of the genome sequence reads corresponding to true Escherichia reads. The computing system may determine, using the second portion of the genome sequence reads, a score indicating a number of true reads for Escherichia. The computing system may select, from a plurality of anti-cancer therapies, the anti-cancer therapy for the cancer subject based on the score.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• C12Q 1/689 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria

Abstract

The present disclosure provides compositions and methods for the detection and treatment of cancer. Specifically, the compositions of the present technology include novel DOTA-haptens that may be complexed with a radioisotope (e.g., 225Ac). Also disclosed herein are methods of the using the DOTA-haptens of the present technology in diagnostic imaging as well as pretargeted radioimmunotherapy.

IPC Classes  ?

• A61K 51/04 - Organic compounds
• A61K 51/10 - Antibodies or immunoglobulinsFragments thereof
• C07F 5/00 - Compounds containing elements of Groups 3 or 13 of the Periodic Table
• C07K 16/32 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against translation products from oncogenes
• C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere

Abstract

Described herein are compositions, uses thereof, and methods for treating a viral infection in a host cell or organism infected by the virus, such as coronaviruses (e.g., severe acute respiratory syndrome coronavirus [SARS-CoV], severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2, the virus and its mutant forms that cause COVID-19], Middle East respiratory syndrome coronavirus [MERS-CoV]), Zika virus, Lassa virus, Crimean Congo hemorrhagic fever virus, hepatitis E virus, and other RNA viruses. Also described herein are synthetic rocaglate compositions, uses thereof, and methods for reducing or inhibiting translation initiation of a messenger ribonucleic acid (mRNA) of a virus in a host cell or organism infected by the virus.

IPC Classes  ?

• A61K 31/343 - Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin having five-membered rings with one oxygen as the only ring hetero atom, e.g. isosorbide condensed with a carbocyclic ring, e.g. coumaran, bufuralol, befunolol, clobenfurol, amiodarone
• A61P 31/14 - Antivirals for RNA viruses

Abstract

The disclosure provides artificial nucleic acid introns configured for selective splicing in cells with aberrant RNA splicing activity, e.g., neoplastic cells. The artificial intron can comprise an upstream flanking exon, an upstream intron, an alternatively spliced “cassette” exon, a downstream intron, and a downstream flanking exon. Also provided are constructs integrating the artificial introns with exons in a configuration that, when the artificial intron is spliced out by the aberrant RNA splicing factors, encode a functional protein. Also disclosed are methods that employ the disclosed platform of selective expression, including, targeted gene therapy methods (e.g., in cancers), diagnostics and imaging, and drug screening.

IPC Classes  ?

• C12N 15/113 - Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides
• A61K 48/00 - Medicinal preparations containing genetic material which is inserted into cells of the living body to treat genetic diseasesGene therapy
• A61P 35/02 - Antineoplastic agents specific for leukemia
• C12N 15/85 - Vectors or expression systems specially adapted for eukaryotic hosts for animal cells

Abstract

The presently disclosed subject matter provides antibodies or antigen-binding fragments thereof that bind to L1CAM and methods of using such antibodies or antigen-binding fragments thereof. The presently disclosed subject further provides immunoconjugates comprising anti-L1CAM antibodies or antigen-binding fragments thereof and methods of using such immunoconjugates.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
• A61P 35/00 - Antineoplastic agents
• G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids

Abstract

The present disclosure provides methods involving the use of modified magnetic beads for molecular archiving of nucleic acid molecules isolated from a biological sample, such as cell-free DNA (cfDNA). The modified magnetic bead compositions disclosed herein efficiently captured nucleic acid molecules (e.g., DNA) via electrostatic catalysis to generate nucleic acid libraries for iterative molecular analysis.

IPC Classes  ?

• C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
• C12Q 1/6811 - Selection methods for production or design of target specific oligonucleotides or binding molecules
• C12Q 1/6844 - Nucleic acid amplification reactions

Abstract

The presently disclosed subject matter provides for antigen-recognizing receptors that specifically target B7-H3 and cells comprising such B7-H3-targeted antigen-recognizing receptors. The presently disclosed subject matter further provides uses of the B7-H3-targeted antigen-recognizing receptors for treatment.

IPC Classes  ?

• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/00 - Antineoplastic agents
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

The present disclosure is directed to leucine zipper-based sorting systems adapted to facilitate the expression and coordination of polypeptide sequences capable of improving the function of CAR T cells. The systems enable the generation of T cells engineered to express multiple combinations of CARs (multi-CAR), safety-switches, switch receptors, and/or cytokines.

IPC Classes  ?

• A61K 35/17 - LymphocytesB-cellsT-cellsNatural killer cellsInterferon-activated or cytokine-activated lymphocytes
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens
• A61P 35/02 - Antineoplastic agents specific for leukemia
• A61P 37/04 - Immunostimulants
• C07K 14/725 - T-cell receptors
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• C12N 5/00 - Undifferentiated human, animal or plant cells, e.g. cell linesTissuesCultivation or maintenance thereofCulture media therefor
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells
• C12N 9/64 - Proteinases derived from animal tissue, e.g. rennin

Abstract

The present invention relates to the use of one or more biomarkers to evaluate the likelihood that a CDK4 inhibitor would produce an anti-cancer effect in a subject. Accordingly, in certain non-limiting embodiments, the present invention provides for methods, compositions and kits for a companion diagnostic for CDK4 inhibitors, and in particular, for the use of the colocalization of Enigma and CDH18 biomarkers to foci within the cancer for determining whether the cancer can be successfully treated by CDK4 inhibition.

IPC Classes  ?

• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

Abstract

Presented herein are systems and methods of generating frames across time from sequence data using machine learning (ML) models. A computing system coupled with memory can retrieve a sequence dataset comprising a plurality of identifiers generated using a gene segment from a subject at a first time. Each identifier of the plurality of identifiers can have an alphanumeric character indicating a base type at a respective position in the gene segment. The computing system can convert the plurality of the identifiers of the sequence dataset to generate a first frame comprising a first plurality of coordinates for the gene segment at the first time. The computing system can provide the first frame as an input to a ML model. The computing system can generate, based on providing the input to the ML model, a second frame comprising a second plurality of coordinates for the gene segment at a second time.

IPC Classes  ?

• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G06N 20/00 - Machine learning
• G06T 7/00 - Image analysis
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• C12Q 1/6874 - Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation [SBH]

Abstract

Presented herein are systems and methods relating to imputing metabolite information. A method includes receiving a first and second metabolite dataset, normalizing, the first dataset and the second dataset, transforming, the normalized first and second datasets, and aggregating the normalized first dataset and the normalized second dataset to generate a first metabolite matrix, the first metabolite matrix missing a first relative abundance value. The method includes decomposing the first metabolite matrix into a second metabolite matrix and a third metabolite matrix to factorize the first metabolite matrix and generating a fourth metabolite matrix that is the product of the second metabolite matrix and the third metabolite matrix, wherein the fourth metabolite matrix including an imputed first relative abundance value.

IPC Classes  ?

• G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

Abstract

Provided are compounds for activating TFEB, the compounds being according to Formula (I) or Formula (II) as well as pharmaceutical compositions including such compounds and methods for using the compounds for the treatment of diseases and disorders.

IPC Classes  ?

• C07D 243/06 - Heterocyclic compounds containing seven-membered rings having two nitrogen atoms as the only ring hetero atoms having the nitrogen atoms in positions 1 and 4
• C07D 243/08 - Heterocyclic compounds containing seven-membered rings having two nitrogen atoms as the only ring hetero atoms having the nitrogen atoms in positions 1 and 4 not condensed with other rings
• A61K 31/496 - Non-condensed piperazines containing further heterocyclic rings, e.g. rifampin, thiothixene or sparfloxacin
• A61K 31/33 - Heterocyclic compounds

Abstract

The present technology relates to methods, computing devices, and systems for predicting the fitness of mutant p53 based on the loss of transcription factor function and immunogenicity of a particular TP53 mutation. The fitness of mutant p53 may be used to determine whether a patient will benefit from a particular anti-cancer therapy such as immune checkpoint inhibitor therapy, adoptive T-cell therapy, or prophylactic cancer vaccine therapy.

IPC Classes  ?

• G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
• C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
• G16B 15/30 - Drug targeting using structural dataDocking or binding prediction
• G16B 20/10 - Ploidy or copy number detection
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16B 20/50 - Mutagenesis

Abstract

The presently disclosed subject matter provides for methods and compositions for treating cancer (e.g., breast cancer). It relates to mutant PIK3CA-targeted TCRs that specifically target a mutant PIK3CA peptide (e.g., a human mutant PIK3CA peptide), and immunoresponsive cells comprising such TCRs. The presently disclosed mutant PIK3CA peptide-specific TCRs have enhanced immune-activating properties, including anti-tumor activity.

IPC Classes  ?

• C07K 14/725 - T-cell receptors
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/32 - T-cell receptors [TCR]
• A61K 40/42 - Cancer antigens
• A61K 40/50 - Cellular immunotherapy characterised by the use of allogeneic cells

Abstract

SRSF2ZRSR2ZRSR2, wherein the mutations are shared across patients with those mutational genotypes, as well as cognate T cell receptors (TCRs) that specifically recognize those neoantigens.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• A61K 39/395 - AntibodiesImmunoglobulinsImmune serum, e.g. antilymphocytic serum
• G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca

Abstract

Presented herein is a sensor platform which can be used for the detection of arbitrary molecules of interest or for clinical diagnosis using test solutions. The sensor may include an array of environmentally sensitive fluorescent reporter probes. Each probe is placed on a separate pixel or several pixels of the sensor array and has a different coating, such that it responds slightly differently to environmental stimuli than every other pixel upon addition of the test solution.

IPC Classes  ?

• G01N 21/64 - FluorescencePhosphorescence
• G01N 33/53 - ImmunoassayBiospecific binding assayMaterials therefor
• G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
• G01N 33/58 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving labelled substances
• G01N 33/84 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving inorganic compounds or pH
• B82Y 20/00 - Nanooptics, e.g. quantum optics or photonic crystals
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G01N 21/77 - Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator

Abstract

The present disclosure provides methods for accurately predicting the risk of recurrence and metastases in lung cancer (e.g., lung adenocarcinoma) patients using ctDNA and 'spread through air spaces' (STAS) biomarkers.

IPC Classes  ?

• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• G06N 20/00 - Machine learning
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Abstract

Presented herein are systems, methods, and non-transient computer readable media for determining predicted response scores of subjects. A computing system may identify a first feature set for a first subject to be administered with immunotherapy to address a condition. The first feature set may include one or more of: (i) a first radiological feature identified in a tomogram of a section associated with the condition in the first subject, (ii) a first immunohistochemistry (IHC) feature derived from an image of a sample associated with the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted score identifying a response to the immunotherapy to be administered to the first subject.

IPC Classes  ?

• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
• G06T 7/00 - Image analysis
• G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Aspects of the disclosure provide compositions and methods for reversible expression of a target gene. In some aspects, the disclosure provides nucleic acids comprising multiple pairs of recombinase sites, each pair having first and second members flanking an inverted expression cassette encoding a target gene.

IPC Classes  ?

• C12N 9/22 - Ribonucleases
• C12N 15/67 - General methods for enhancing the expression

Abstract

The present technology provides compositions and methods for modulating T Cell Receptor (TCR) specificity as well as methods for treating cancer in a subject in need thereof. The present disclosure provides engineered cytotoxic T cells comprising a TCR and/or nucleic acid encoding the TCR and a mutant CD8 alpha polypeptide and/or nucleic acid encoding the mutant CD8 alpha polypeptide.

IPC Classes  ?

• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/32 - T-cell receptors [TCR]
• A61K 40/42 - Cancer antigens
• C07K 14/705 - ReceptorsCell surface antigensCell surface determinants
• C07K 14/725 - T-cell receptors
• C12N 5/0783 - T cellsNK cellsProgenitors of T or NK cells

Abstract

The present disclosure relates to methods of treating a vascular malformation in a subject expressing a gain-of-function mutation in a PIK3CA gene comprising administering, to the subject, an effective amount of an agent that inhibits phosphoinositide 3-kinase (“PI3K”).

IPC Classes  ?

• A61K 31/5377 - 1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
• A61K 31/427 - Thiazoles not condensed and containing further heterocyclic rings
• A61K 31/4375 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system containing a six-membered ring having nitrogen as a ring hetero atom, e.g. quinolizines, naphthyridines, berberine, vincamine
• A61K 31/4439 - Non-condensed pyridinesHydrogenated derivatives thereof containing further heterocyclic ring systems containing a five-membered ring with nitrogen as a ring hetero atom, e.g. omeprazole
• A61K 31/444 - Non-condensed pyridinesHydrogenated derivatives thereof containing further heterocyclic ring systems containing a six-membered ring with nitrogen as a ring hetero atom, e.g. amrinone
• A61K 31/519 - PyrimidinesHydrogenated pyrimidines, e.g. trimethoprim ortho- or peri-condensed with heterocyclic rings
• A61P 9/00 - Drugs for disorders of the cardiovascular system

Abstract

Described herein are systems and methods for intracellular imaging, assessment, and/or treatment of tissue before, during, and/or after surgical procedures using nanoparticles (e.g., less than 50 nanometers in diameter, e.g., photoswitchable nanoparticles) and/or a super-resolution microscope system. The present disclosure describes nanoparticles (e.g., nanosensors and photoswitchable nanoparticles) that are used to monitor and/or track changes in environmental conditions and/or analytes in the cellular microenvironment before, during, and/or after surgical procedures. The present disclosure also describes systems and methods that provide information related to the distribution and/or delivery of photoswitchable nanoparticles at super resolution (e.g., using super-resolution microscopy).

IPC Classes  ?

• A61K 49/00 - Preparations for testing in vivo
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
• A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value using optical sensors, e.g. spectral photometrical oximeters
• A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• A61K 47/69 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the conjugate being characterised by physical or galenical forms, e.g. emulsion, particle, inclusion complex, stent or kit

Abstract

Provided herein is a method comprising: obtaining a sample comprising fixed cells or fixed nuclei, hybridizing oligonucleotide probes to RNA in the fixed cells or fixed nuclei, to produce labeled cells or labeled nuclei, enriching for a sub-population of the fixed cells or fixed nuclei based on their labeling, by flow cytometry, treating the enriched cells or enriched nuclei with a double-stranded DNAse for a sufficient time to degrade the oligonucleotide probes, inactivating the double-stranded DNAse, and performing single cell RNA analysis on the cells or nuclei.

IPC Classes  ?

• C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
• C12Q 1/6813 - Hybridisation assays
• C12Q 1/6806 - Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
• C12N 15/11 - DNA or RNA fragmentsModified forms thereof
• C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA

Abstract

The present disclosure is directed to systems and methods for identifying regions of interest (ROIs) in images. A computing system may identify an image including an annotation defining an ROI. The image may have a plurality of pixels in a first color space. The computing system may convert the plurality of pixels from the first color space to a second color space to differentiate the annotation from the ROI. The computing system may select a first subset of pixels corresponding to the annotation based at least on a color value of the first subset of pixels in the second color space. The computing system may identify a second subset of pixels included in the ROI from the image using the first subset of pixels. The computing system may store an association between the second subset of pixels and the ROI defined by the annotation in the image.

IPC Classes  ?

• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 5/70 - DenoisingSmoothing
• G06T 7/00 - Image analysis
• G06T 7/194 - SegmentationEdge detection involving foreground-background segmentation
• G06V 30/14 - Image acquisition
• G06V 30/18 - Extraction of features or characteristics of the image
• G06V 30/19 - Recognition using electronic means

Abstract

The present invention relates to compositions and methods employing conjugates that include a self-assembly and disassembly (SADA) polypeptide and a binding domain. The present invention encompasses the recognition that conjugates with a SADA polypeptide have certain improved biological properties. SADA-conjugates are described, along with uses thereof (e.g., as therapeutic or diagnostic agents) and methods of manufacture.

IPC Classes  ?

• A61K 47/64 - Drug-peptide, drug-protein or drug-polyamino acid conjugates, i.e. the modifying agent being a peptide, protein or polyamino acid which is covalently bonded or complexed to a therapeutically active agent
• A61K 47/54 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an organic compound
• A61K 51/04 - Organic compounds
• A61K 51/10 - Antibodies or immunoglobulinsFragments thereof
• C07K 14/47 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from animalsPeptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from humans from vertebrates from mammals
• C07K 14/715 - ReceptorsCell surface antigensCell surface determinants for cytokinesReceptorsCell surface antigensCell surface determinants for lymphokinesReceptorsCell surface antigensCell surface determinants for interferons
• C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
• C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere

Abstract

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

IPC Classes  ?

• G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
• G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 30/24 - Character recognition characterised by the processing or recognition method

Abstract

Provided herein are chimeric innate receptor (OR) fusion proteins that enhance the persistence of NK and NK-like T cells in response to AML antigens, such as CD33. The OR fusion proteins of the present technology are useful in methods for treating cancer, such as AML. The OR fusion proteins of the present technology are useful in methods for treating AML.

IPC Classes  ?

• A61K 40/42 - Cancer antigens
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants

Abstract

The present application is directed to multiplex intein-based methods and compositions for the generation engineered cells expressing modular polypeptides, for example CARs and CCRs.

IPC Classes  ?

• C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
• A61K 40/11 - T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cellsLymphokine-activated killer [LAK] cells
• A61K 40/31 - Chimeric antigen receptors [CAR]
• A61K 40/42 - Cancer antigens

Abstract

The present disclosure relates to compounds according to Formula I: or a pharmaceutically acceptable salt thereof, as well as compositions including such compounds and uses thereof. As evidenced by this application, these compounds and compositions are suitable for, among other things, treating cancer.

IPC Classes  ?

• C07F 5/02 - Boron compounds
• A61K 47/68 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the non-active ingredient being a modifying agent the modifying agent being an antibody, an immunoglobulin or a fragment thereof, e.g. an Fc-fragment
• A61K 47/69 - Medicinal preparations characterised by the non-active ingredients used, e.g. carriers or inert additivesTargeting or modifying agents chemically bound to the active ingredient the non-active ingredient being chemically bound to the active ingredient, e.g. polymer-drug conjugates the conjugate being characterised by physical or galenical forms, e.g. emulsion, particle, inclusion complex, stent or kit
• A61P 35/00 - Antineoplastic agents

Abstract

Presented herein are systems, devices, and methods for shortwave infrared (SWIR) fluorescence imaging. The device may include a housing defining an aperture, an optical component having a first end structured to be optically coupled with the aperture of housing, a second end structured to be distal from the housing, the second end configured to accept at least a portion of light having a SWIR wavelength emitted by a fluorophore in at least a target area of an object, and a body configured to convey the light from the second end to the first end, and a sensor disposed within the housing, the sensor optically coupled with the optical component via the aperture, the sensor configured to convert the light to an electrical signal corresponding to the light.

IPC Classes  ?

• G01N 21/64 - FluorescencePhosphorescence
• G01N 21/359 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light