Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Systems in a flow cytometer having an interrogation zone and illumination impinging the interrogation zone include: a lens subsystem including a collimating element that collimates light from the interrogation zone, a light dispersion element that disperses collimated light into a light spectrum, and a focusing lens that focuses the light spectrum onto an array of adjacent detection points; a detector array, including semiconductor detector devices, that collectively detects a full spectral range of input light signals, in which each detector device detects a subset spectral range of the full spectral range of light signals; and a user interface that enables a user to create a set of virtual detector channels by grouping detectors in the detector array, such that each virtual detector channel corresponds to a detector group and has a virtual detector channel range including the sum of subset spectral ranges of the detectors in the corresponding detector group.

Disclosed herein are system, method, and computer program product embodiments for determining phenotypic impacts of molecular variants identified within a biological sample. Embodiments include receiving molecular variants associated with functional elements within a model system. The embodiments then determine molecular scores associated with the model system. The embodiments then determine molecular signals and population signals associated with the molecular variants based on the molecular scores. The embodiments then determine functional scores for the molecular variants based on statistical learning. The embodiments then derive evidence scores of the molecular variants based on the functional scores. The embodiments then determine phenotypic impacts of the molecular variants based on the functional scores or evidence scores.

The present invention teaches a method of predicting the potential for manifestation of various medical conditions by analyzing human placenta. The method contemplates and includes determining the need for early monitoring, intervention or potential treatment for medical conditions likely to manifest as a child grows older and investigating the potential for various medical conditions. The method includes selecting and identifying a sample of the placenta to analyze by computer applied mathematical algorithms and preparing the placental sample to be analyzed by various procedures. Then the placental sample is captured by obtaining a three-dimensional digital image of the chorionic surface of the placental sample by use of a selected capturing device. The digital image is corrected for errors inherent in digital image acquisition and the resultant image data is loaded into a computer for analysis. The computer performs an analysis on the corrected digital image data using one or more algorithms to determine the vascular structure of the placenta, and the resultant data is interpreted and analyzed to determine the potential for manifestation of various medical conditions.

The present invention includes methods, systems, and kits, for identifying and modifying the treatment of a systemic lupus erythematosus (SLE) patient prior to the presence of autoantibodies, comprising: (a) obtaining a dataset representing protein expression level values for cytokines and molecules; (b) assessing the dataset for protein expression levels of at least one innate serum mediator; (c) assessing the dataset for protein expression levels of at least one adaptive serum mediator; and (d) determining the likelihood that the patient will develop SLE prior to the onset of autoantibodies when compared to a control.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

The present invention relates to a method for discovery of a novel miRNA biomarker for cancer diagnosis, a biomarker for diagnosis of bile duct cancer or pancreatic cancer which has been discovered through the method for discovery of a biomarker, a method for diagnosing cancer, comprising a step in which cancer is diagnosed when f(x)>0 by substitution of the expression level of the miRNA biomarker, which is detected by the method for discovery of a miRNA biomarker for cancer diagnosis, in a sample into a novel SVM classifier function, a kit for diagnosing bile duct cancer or pancreatic cancer comprising the biomarker for diagnosing bile duct cancer or pancreatic cancer, and a computing device for performing a process of diagnosing cancer when f(x)>0 as a result of a calculation by substitution of the expression level of a miRNA biomarker, which is detected by the method for discovery of a miRNA biomarker for cancer diagnosis, into the novel SVM classifier function.

Markers and methods useful for assessing coronary artery disease in a subject are provided, along with related kits, systems, and media. Also provided are predictive models, based on the markers, as well as computer systems, and software embodiments of the models for scoring and optionally classifying samples.

Techniques for training a statistical model for determining whether a subject is likely to respond to a checkpoint blockade therapy. The techniques include obtaining, for each subject in a plurality of subjects having responders to a checkpoint blockade therapy and non-responders to the therapy, expression data indicating expression levels for a plurality of genes; determining, for the plurality of genes, expression level differences between the responders and the non-responders using the expression data; identifying, using the determined expression level differences, a subset of genes associated with a therapy in the plurality of genes; training, using the expression data, a statistical model for predicting efficacy of the therapy, the training comprising: identifying at least some of the subset of genes as a predictor set of genes to include in the statistical model; and estimating, using the expression data, parameters of the statistical model associated with the predictor set of genes.

Techniques for determining whether a subject is likely to respond to an immune checkpoint blockade therapy. The techniques include obtaining expression data for the subject, using the expression data to determine subject expression levels for at least three genes selected from the set of predictor genes consisting of BRAF, ACVR1B, MPRIP, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, SNX6, ACVR1B, MPRIP, COPS3, NLRX1, ELAC2, MON1B, ARF3, ARPIN, SPRYD3, FLI1, TIRAP, GSE1, POLR3K, PIGO, MFHAS1, NPIPA1, DPH6, ERLIN2, CES2, LHFP, NAIF1, ALCAM, SYNE1, SPINT1, SMTN, SLCA46A1, SAP25, WISP2, TSTD1, NLRX1, NPIPA1, HIST1H2AC, FUT8, FABP4, ERBB2, TUBA1A, XAGE1E, SERPINF1, RAI14, SIRPA, MT1X, NEK3, TGFB3, USP13, HLA-DRB4, IGF2, and MICAL1; and determining, using the determined expression levels and a statistical model trained using expression data indicating expression levels for a plurality of genes for a plurality of subjects, whether the subject is likely to respond to the immune checkpoint blockade therapy.