Abstract: Methods for predicting risk of heart transplant rejection are disclosed.

Abstract: The present disclosure in some embodiments relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including obtaining one or more digitized endomyocardial biopsy (EMB) images from a patient having had a heart transplant; extracting a plurality of histological features from the one or more digitized EMB images; and applying a machine learning predictive model to operate on the plurality of histological features to generate a prediction for the patient. The prediction includes a grade or a clinical trajectory associated with the patient.

Abstract: A recombinant viral vector comprising an expression cassette which comprises a coding sequence for an shRNA inhibitor of vasohibin (VASH)-small vasohibin binding protein (SVBP) complex operably linked to regulatory sequences which direct expression thereof is provided. Further provided are compositions containing such viral vectors formulated for delivery to a human patient. Also provided are methods using these vectors and compositions for improving or stabilizing cardiac function.

Abstract: Methods, apparatus, and other embodiments predict heart failure from WSIs of cardiac histopathology using a deep learning convolutional neural network (CNN). One example apparatus includes a pre-processing circuit configured to generate a pre-processed WSI by downsampling a digital WSI; an image acquisition circuit configured to randomly select a set of non-overlapping ROIs from the pre-processed WSI, and configured to provide the set of non-overlapping ROIs to a deep learning circuit; a deep learning circuit configured to generate an image-level probability that a member of the set of non-overlapping ROIs is a failure/abnormal pathology ROI using a CNN; and a classification circuit configured to generate a patient-level probability that the patient from which the region of tissue represented in the WSI was acquired is experiencing failure or non-failure based, at least in part, on the image-level probability.

Abstract: Described herein is a method for improving or stabilizing cardiac function by inhibiting tubulin carboxypeptidase (TCP). Also described herein is a method for treating heart failure in humans comprising dosing a patient with a therapeutic which interferes with detyrosinated microtubules in cardiomyocytes. Also provided are viral vectors which comprise a nucleic acid encoding a tubulin tyrosine ligase (TTL) gene under the control of regulatory elements direct expression thereof. Compositions are also provided which contain such viral vectors formulated for delivery to a human patient.

Abstract: Methods, apparatus, and other embodiments predict heart failure from WSIs of cardiac histopathology using a deep learning convolutional neural network (CNN). One example apparatus includes a pre-processing circuit configured to generate a pre-processed WSI by downsampling a digital WSI; an image acquisition circuit configured to randomly select a set of non-overlapping ROIs from the pre-processed WSI, and configured to provide the set of non-overlapping ROIs to a deep learning circuit; a deep learning circuit configured to generate an image-level probability that a member of the set of non-overlapping ROIs is a failure/abnormal pathology ROI using a CNN; and a classification circuit configured to generate a patient-level probability that the patient from which the region of tissue represented in the WSI was acquired is experiencing failure or non-failure based, at least in part, on the image-level probability.

Abstract: Methods for predicting a propensity for heart or kidney failure in a diabetic or pre-diabetic individual by determining the amount and/or molecular weight of islet amyloid polypeptide present in a sample from the individual are provided.