9 results about "Cellular phenotype" patented technology

The present invention relates generally to compositions, systems and methods for modulating hepatitis B virus (HBV) by targeting gene repression. In particular, the present invention provides an epigenetically modified DNA targeting system, such as a CRISPR-Cas / guide RNA (gRNA) system, for transcriptional repression of hepatitis B virus genes to promote cell phenotypes that lead to reduced HBV infection. In some embodiments, the epigenetically modified DNA targeting systems of the present invention bind to or target a target site of at least one gene in the hepatitis B virus DNA sequence or a regulatory element thereof in a cell. In some aspects, the systems provided herein relate to transcriptional repression of one or more hepatitis B virus genes and / or regulatory elements thereof. In some aspects, the invention also provides methods and uses associated with the provided compositions, such as in repression of hepatitis B virus replication and expression associated with hepatitis B infection.

A composition is provided comprising a cell population and pharmaceuticals derived from human gingival fibroblasts, present in proportions not found in natural gingival tissue, but having a cellular phenotype preferentially selected to express proteins favorable for angiogenesis and anti-inflammatory effects, while reducing the formation of cell populations that promote tumorigenesis and / or metalloproteinases that inhibit tissue regeneration. A method for producing such a composition that increases proliferation several times over compared to previously known methods is provided, and a method for using such a composition in a wide range of human cell therapies is also provided.

Techniques for identifying a cellular phenotype indicative of a condition of a subject are described. The techniques can include identifying sequence read data indicating sequences of DNA fragments from a sample from a subject; determining, based on the sequence read data, endpoint positions of the DNA fragments; determining input features based on the endpoint positions of the DNA fragments; and predicting, using a classifier and based on the input features, a cellular phenotype indicative of a condition, such as a cancer, within the subject.

Certain embodiments of the invention are directed to evaluating and identifying cells by recording and interpreting a time-dependent signal produced by unique cell respiration and permeability attributes of isolated viable cells. Some methods comprise dividing the sample into two or more sub-samples or sample portions, mixing each sub-sample or sample portion with one or more reagents and / or one or more reactants forming distinct sub-sample or sample portion mixtures, compartmentalizing each of the sub-sample or sample portion mixtures into a plurality of small volume compartments, monitoring characteristics of the small volume compartments over time and collecting compartment data, and transmitting the collected data to at least one neural network.

A method for predicting drug pathways that modulate cellular phenotypes is provided. The method includes obtaining a logic-based differential equation model of a signaling network relating to a cellular phenotype, comprising nodes representing proteins or mRNAs and edges representing interactions. The method further includes identifying candidate compounds affecting the cellular phenotype, determining protein targets using a drug-target database, and mapping pathways from protein targets to network nodes by searching a protein interaction database and computing ranked pathways by minimizing a cost function based on edge weights. The method includes expanding the model by incorporating mapped pathways, simulating expanded model variants to predict compound effects on the cellular phenotype, and outputting predictions of how candidate compounds modulate the cellular phenotype through identified pathways.

The present disclosure provides a method for screening cells, the method including a step of preparing a plurality of cells which are tagged with a first barcode nucleic acid associated with a test target and treated with the test target, a step of sorting the plurality of cells based on cellular phenotype using an imaging cell sorter, and a step of identifying the test target used to treat each cell using the first barcode nucleic acid as an indicator.

The present disclosure is related to systems and methods to be used as aids in the diagnosis of risk for oral cancer by identifying cellular phenotype characteristics of a noninvasive brush biopsy sample.

Provided herein are epigenetic-modifying DNA-targeting systems, such as CRISPR-Cas / guide RNA (gRNA) systems, for the transcriptional repression of Hepatitis B viral (HBV) genes to promote a cellular phenotype that leads to the reduction of HBV infection. In some embodiments, the epigenetic-modifying DNA-targeting systems bind to or target a target site of at least one gene or regulatory element thereof in a Hepatitis B viral DNA sequence in cell. In some aspects, the provided systems relate to the transcriptional repression of one or more Hepatitis B viral gene and / or regulatory element thereof. In some aspects, also provided herein are methods and uses related to the provided compositions, for example in repressing Hepatitis B viral replication and expression in connection with Hepatitis B infections.

A system and method for predictive modeling of cellular systems assesses compound effectiveness on cellular metabolic and inflammatory states. Cellular samples are exposed to metabolic MRX sensors detecting oxidative stress, reporting cytoskeletal organization, or monitoring mitochondrial morphology, dynamics, or compartment-specific redox activity. Images of MRX sensor-labeled samples are captured using an imaging device and analyzed by trained predictive models to determine compound impact on immunological activation, inflammatory status, oxidative stress, mitochondrial function, and bio-energetic state. The system supports multisensor integration and spectral unmixing to generate multidimensional metabolic or ROS related MRX fingerprints that characterize cellular phenotypes, drug responses, and enable comparison to reference states. Preprocessing modules perform image normalization, augmentation, segmentation, and feature extraction to enhance model performance. Predictive models classify treatment conditions, identify perturbation-specific signatures, and enable compound ranking or mechanism-of-action inference. The system provides an objective, scalable, and high-content platform for drug screening, toxicity assessment, and inflammation research.