Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions. The computer readable medium is configured to cause a processor to access an image volume of a rectum comprising a rectal tumor. A forward mapping is generated based on non-rigidly registering a healthy rectal atlas to the image volume. The forward mapping is inverted to generate an inverse mapping from the image volume to the healthy rectal atlas. Based on the inverse mapping, a plurality of deformation vectors, associated with a deformation within a rectal wall of the rectum, are determined. Magnitude based deformation features and orientation based deformation features are computed from the plurality of deformation vectors. One or more of the magnitude based deformation features and one or more of the orientation based deformation features are utilized to determine a response of a patient to a chemoradiation treatment.

Abstract: A method for multi-dimensional, relaxation-diffusion magnetic resonance fingerprinting (MRF) includes performing, using a magnetic resonance imaging (MRI) system, a pulse sequence that integrates free-waveform b-tensor diffusion encoding into a magnet resonance fingerprinting pulse sequence to perform a multi-dimensional, relaxation-diffusion encoding while acquiring MRF signal evolutions, processing, using a processor, the acquired MRF signal evolutions to determine at least one relaxation parameter and at least one diffusivity parameter, and generating, using the processor, a report including at least one of the at least one relaxation parameter and the at least diffusivity parameter.

Abstract: A neuromodulation apparatus for stimulating neural activity in a renal nerve of a patient is provided, the apparatus comprising a transducer Patient for applying a signal to the renal nerve so as to produce a physiological response. Methods of treating sleep apnoea are also provided, including methods using the neuromodulation apparatus.

Abstract: The present application provides a system and method for quantifying perfusion using a dictionary matching approach. In some aspects, the method comprises performing a predetermined pulse sequence using an MRI system to acquire MRI data from the subject after having delivered a dose of a contrast agent to the subject. The method also includes comparing the MRI data to a dictionary to determine perfusion information, and generating, using the perfusion information, a report indicative of perfusion within the subject.

Abstract: Compounds and methods of modulating 15-PGDH activity, modulating tissue prostaglandin levels, treating disease, diseases disorders, or conditions in which it is desired to modulate 15-PGDH activity and/or prostaglandin levels include 15-PGDH inhibitors described herein.

Abstract: Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Abstract: Embodiments discussed herein facilitate building and/or employing model(s) for determining tumor prognoses based on a combination of radiomic features and pathomic features. One example embodiment can perform actions comprising: providing, to a first machine learning model, at least one of: one or more intra-tumoral radiomic features associated with a tumor or one or more peri-tumoral radiomic features associated with a peri-tumoral region around the tumor; receiving a first predicted prognosis associated with the tumor from the first machine learning model; providing, to a second machine learning model, one or more pathomic features associated with the tumor; receiving a second predicted prognosis associated with the tumor from the second machine learning model; and generating a combined prognosis associated with the tumor based on the first predicted prognosis and the second predicted prognosis.

Abstract: Embodiments discussed herein facilitate implementation of one or more DBS pulsing strategies that maximize synaptic suppression with the minimum number of stimuli. One example embodiment comprises a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: applying deep brain stimulation (DBS) electrical stimulation according to a first mode to cause steady-state excitatory post-synaptic current (EPSC) suppression in a set of synapses; and applying DBS electrical stimulation according to a second mode that is different than the first mode to maintain EPSC suppression in the set of synapses.

Abstract: Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.

Abstract: Embodiments discussed herein facilitate building and/or employing a clinical-radiomics score for determining tumor prognoses based on a combination of a radiomics risk score generated by a machine learning model and clinico-pathological factors. One example embodiment can perform actions comprising: accessing a medical imaging scan of a tumor; segmenting a peri-tumoral region around the tumor; extracting one or more intra-tumoral radiomic features from the tumor and one or more peri-tumoral radiomic features from the peri-tumoral region; providing the one or more intra-tumoral radiomic features and the one or more peri-tumoral radiomic features to a trained machine learning model; receiving a radiomic risk score (RRS) associated with the tumor from the machine learning model; determining a clinical-radiomics score based on the RRS and one or more clinico-pathological factors; and generating a prognosis for the tumor based on the clinical-radiomics score.

Abstract: An insulin composition comprises an insulin analogue and polymer blend. The insulin analogue contains cysteine substitutions at positions B4 and A10 (to form cystine B4-A10), and one or more additional substitutions selected from the group consisting of: a connecting domain of 5-11 amino acids between insulin A- and B domains; a non-beta-branched amino-acid substitution at position A8; a non-beta-branched acidic or polar side chain at position A14; a halogenic modification of PheB24 at the ortho position; and substitution of lysine at position B29 by Glu, Ala, Val, Ile, Leu, amino-propionic acid, amino-butryic acid, or Norleucine. The insulin analogue is compatible with a process of manufacture that includes one or more steps within the temperature range 90-120° C. The encapsulated insulin analogue may optionally contain free PEG or be PEGylated. The insulin analogue-encapsulated polymer blend may be cast as a microneedle patch for topical administration or as micropellets for subcutaneous injection.

Abstract: Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.

Abstract: Systems, methods, and media for displaying interactive augmented reality presentations are provided. In some embodiments, a system comprises: a plurality of head mounted displays, a first head mounted display comprising a transparent display; and at least one processor, wherein the at least one processor is programmed to: determine that a first physical location of a plurality of physical locations in a physical environment of the head mounted display is located closest to the head mounted display; receive first content comprising a first three dimensional model; receive second content comprising a second three dimensional model; present, using the transparent display, a first view of the first three dimensional model at a first time; and present, using the transparent display, a first view of the second three dimensional model at a second time subsequent to the first time based one or more instructions received from a server.

Abstract: Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.

Abstract: A system for displaying and interacting with magnetic resonance imaging (MRI) data acquired using an MRI system includes an image reconstruction module configured to receive the MRI data and to reconstruct a plurality of images using the MRI data, an image rendering module coupled to the image reconstruction module and configured to generate at least one multidimensional image based on the plurality of images and a user interface device coupled to the image rendering module and located proximate to a workstation of the MRI system. The user interface device is configured to display the at least one multidimensional image in real-time and to facilitate interaction by a user with the multidimensional image in a virtual reality or augmented reality environment.

Abstract: Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

Abstract: Provided herein are methods and a device for collecting biological samples from a subject. In particular, the present disclosure relates to a device designed to specifically capture samples within the target areas of a patient for specific cell collection for particular diagnosis.

Abstract: Provided herein are methods and a device for collecting biological samples. In particular, the present disclosure relates to a device designed to specifically capture samples within the target areas of a patient for specific cell collection for particular diagnosis.

Abstract: Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.

Abstract: A method for temperature quantification using magnetic resonance fingerprinting (MRF) includes acquiring MRF data from a region of interest in a subject using an MRF pulse sequence with smoothly varying RF phase for MR resonant frequencies that is played out continuously. For each of a plurality of time intervals during acquisition of the MRF data the method further includes comparing a set of the MRF data associated with the time interval to an MRF dictionary to determine at least one quantitative parameter of the acquired MRF data, determining a temperature change based on the at least one quantitative parameter and generating a quantitative map of the temperature change in the region of interest. The region of interest can include aqueous and adipose tissue.