Abstract: The present disclosure provides methods and devices for sample extraction.

Abstract: Disclosed herein are systems, apparatuses, and methods for forming micro-organospheres. In some variations, a system may comprise a micro-organosphere generator configured to form a set of micro-organospheres from a mixture of a biological sample and a fluid. A controller may be coupled to an imaging device. The controller may be configured to receive the imaging data corresponding to one or more of the mixture or the set of micro-organospheres, and estimate one or more characteristics of the set of micro-organospheres based at least on the imaging data.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: The present disclosure provides methods and devices for sample extraction.

Abstract: The present disclosure describes, in part, a Micro-organosphere immune-oncology assay and methods of making and using same. The assay quickly measures the potency of effector immune cells, such as tumor infiltrating lymphocytes, at killing a patient's tumor cells. Understanding the potency of effector immune cells is critical for adoptive T cell therapy.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Systems, media, methods, and kits disclosed herein can improve analysis capabilities of genomic materials. Results from such analyses can be used to detect genomic biomarkers in one or more genomic materials. The systems, media, methods and kits disclosed herein can identify changes or patterns among samples, and can employ machine learning methods to explore changes or potential changes in biological conditions or risks thereof. Further, the systems, media, methods and kits disclosed herein can utilize machine learning algorithms to analyze samples with high accuracy.

Abstract: Micro-Organosphers, including Patient-Derived Micro-Organospheres (PMOS s), apparatuses and methods of making them, and apparatuses and methods of using them. Also described herein are methods and systems for screening a patient using these Patient-Derived Micro-Organospheres, including personalized therapies.

Abstract: Micro-Organospheres, including Patient-Derived Micro-Organospheres (PMOSs), apparatuses and methods of making them, and apparatuses and methods of using them. Also described herein are methods and systems for screening a patient using these Patient-Derived Micro-Organospheres, including personalized therapies.

Abstract: Micro-Organosphers, including Patient-Derived Micro-Organospheres (PMOSs), apparatuses and methods of making them, and apparatuses and methods of using them. Also described herein are methods and systems for screening a patient using these Patient-Derived Micro-Organospheres, including personalized therapies.

Abstract: Disclosed herein are high-throughput sample processing systems and waste management systems, and methods of using the same.

Abstract: Precision drug screening methods and apparatuses for personalized cancer therapies include the formation of a library of mature Micro-Organospheres, including Patient-Derived Micro-Organospheres (PMOSs), from a single patient tissue sample, such as from a tumor sample, are described. Also described herein are methods and systems for screening a patient using these Patient-Derived Micro-Organospheres, including personalized therapies.

Abstract: Disclosed herein are high-throughput sample processing systems and waste management systems, and methods of using the same.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Systems, media, methods, and kits disclosed herein can be used to analyze human microbiota for the detection of a condition (e.g., a disease or condition). Further, the systems, media, methods, and kits disclosed herein can utilize machine learning algorithms to analyze samples with high accuracy. In an aspect, a classifier capable of distinguishing a population of subjects based on microbiome composition may comprise: a plurality of microbiome-associated features associated with two or more classes of subjects inputted into a machine learning model, wherein the features comprise the microbiome species and abundance of microbiome elements, wherein the features are derived from a taxonomic community composition analysis of a cell-free nucleic acid sample in a population of subjects; wherein the features contribute to a classifier sensitivity of greater than 50% and a classifier specificity of greater than 85% to distinguish the population of subjects into two or more classes.

Abstract: Systems, media, methods, and kits disclosed herein can improve analysis capabilities of genomic materials. Results from such analyses can be used to detect genomic biomarkers in one or more genomic materials. The systems, media, methods and kits disclosed herein can identify changes or patterns among samples, and can employ machine learning methods to explore changes or potential changes in biological conditions or risks thereof. Further, the systems, media, methods and kits disclosed herein can utilize machine learning algorithms to analyze samples with high accuracy.

Abstract: Micro-Organosphers, including Patient-Derived Micro-Organospheres (PMOS s), apparatuses and methods of making them, and apparatuses and methods of using them. Also described herein are methods and systems for screening a patient using these Patient-Derived Micro-Organospheres, including personalized therapies.

Abstract: The present disclosure provides methods and devices for sample extraction.

Abstract: Disclosed herein are high-throughput sample processing systems and waste management systems, and methods of using the same.