Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Fluidic multiwell bioreactors are provided as a microphysiological platform for in vitro investigation of multi-organ crosstalks with microbiome for an extended period of time of at least weeks and months. The platform has one or more improvements over existing bioreactors, including on-board pumping for pneumatically driven fluid flow, a redesigned spillway for self-leveling from source to sink, a non-contact built-in fluid level sensing device, precise control on fluid flow profile and partitioning, and facile reconfigurations such as daisy chaining and multilayer stacking. The platform supports the culture of multiple organs together with microbiome in a microphysiological, interacted systems, suitable for a wide range of biomedical applications including systemic toxicity studies and physiology-based pharmacokinetic and pharmacodynamic predictions. A process to fabricate the bioreactors is also provided.

Abstract: The present invention comprises methods for detecting, monitoring and treating lymphedema by using imaging devices to generate patient anatomical information from which body part volume measurements or geometries can be derived. Yet further, the present invention comprises methods of fitting compression garments for treatment of lymphedema by using the anatomical measurements derived from the patient anatomical information.