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: In embodiments of the invention, the invention provides a method for distinguishing between lymphoma types based on gene expression measurements. In embodiments, the invention distinguishes between PMBCL and DLBCL based on gene expression signatures, and can further distinguish between DLBCL subtypes. In embodiments of the invention, the distinctions are used in methods of treatment.

Abstract: A branched and fenestrated prosthesis may include a main tubular graft body including a proximal end opening, a distal end opening, a lumen, and a sidewall. A branch may extend from the sidewall and may include a first end opening, a second end opening, and a lumen. A fenestration may be disposed in the sidewall and positioned distal of the second end opening of the branch. The branched and fenestrated prosthesis may include a plurality of branches and a plurality of fenestrations.

Abstract: This disclosure relates generally to systems and methods for characterizing a behavior of an anatomical structure. Tracking data can be generated by a tracking system to represent at least a location of at least one sensor in a three-dimensional tracking coordinate system over time. A motion model is generated to characterize the behavior of the anatomical structure over a plurality of time instances. For instance, the motion model includes at least one free parameter and a temporal parameter. Each free parameter estimating geometry of the anatomical structure derived from the tracking data, and the temporal parameter indexes the free parameter over the plurality of time instances. A visualization is generated to provide a sequence of graphical images based on the motion model to characterize behavior of the anatomical structure over time.

Abstract: A method of treating a T-cell mediated disease in a subject by administering to the subject a therapeutically effective amount of an antibody or fragment thereof that specifically binds to CD6 is described. Humanized antibodies useful for the method are also described.

Abstract: A trackable guidewire apparatus and method for use are described. Longitudinally spaced proximal and distal guidewire ends are separated by a guidewire body. A plurality of longitudinally spaced position sensors are configured to provide signals corresponding to a three-dimensional position of at least one position sensor in a coordinate system of an associated tracking system in response to an electromagnetic field/stimulus. At least one retention mechanism is provided for maintaining the medical device in a predetermined retention position longitudinally along the guidewire body. At least one stop structure is provided in a predetermined stop position longitudinally along the guidewire body.

Abstract: A system for monitoring surgical objects is provided. An entry scanner captures a first set of image data from a surgical object identifier. A containment surface defines a target field of view. An exit scanner captures a second set of image data from a surgical object identifier within the target field of view. A monitoring system is electrically connected to at least one of the entry and exit scanners. The monitoring system has a surgical object recognition module having a database of pre-existing surgical object identifier data. The surgical recognition module identifies at least one of the number of surgical objects, the type of surgical objects, and the number of each type of surgical objects from the first and second sets of image data by comparing the first and second sets of image data with the pre-existing surgical object identifier data.

Abstract: Systems and methods for performing psychological assessment are provided. An initial visual stimulus is selected and a series of steps are iteratively performed until associated confidence values for each of a plurality of indices meet a threshold value. The selected visual stimulus is provided at the display. Gaze tracking data is received from a remote eye tracking device in response to the selected stimulus being displayed to a patient. A deviation of the gaze tracking data from a standard response is determined. A score and the associated confidence value for at least one of the plurality of indices are adjusted based on the determined deviation of the gaze tracking data from the standard response. A new visual stimulus is selected based on the confidence values of the plurality of indices. Respective scores for the plurality of indices are provided to a user.

Abstract: Systems and methods provide for detection and controlled interaction with one or more objects. The system can include an imaging subsystem (20), a tool subsystem (26) containing one or more tools, a stage subsystem (16) and a control system (40). The control system (40) can integrate controls for each of the other subsystems, which controls can be implement desired functions over a variety of process parameters to perform the controlled interaction.

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: 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: Provided herein are compositions, systems, kits, and methods for treating a subject with cancer by administering a BACE1 inhibitor, such as MK-8931. In particular embodiments, the subject is treated with radiation (e.g., low dose radiation) first, and then administered a BACE1 inhibitor within a certain time window (e.g., about 3 hours to 6 days after the radiation treatment).

Abstract: Described herein is a system for remote monitoring of non-critically ill hospitalized patients. The system can include a graphical user interface displaying a representation of the patient census. Each patient is represented by a graphic showing a clinical status, with an underlying expanded view of various parameters. The system can also include a non-transitory memory storing computer executable instructions and a processor configured to execute the computer executable instructions. Upon execution, the computer executable instructions can at least: receive a data stream associated with a non-critically ill hospitalized patient; determine a risk score associated with the non-critically ill hospitalized patient based on data associated with the alarm notification, wherein the risk score represents a probability of the non-critically ill hospitalized patient developing an actionable clinical event; and rearrange the plurality of graphical representations on the GUI based on the risk score.

Abstract: Compositions and methods for immunization against human breast cancer are disclosed. A breast cancer vaccine comprises an immunogenic polypeptide comprising human ?-lactalbumin.

Abstract: A method is provided for implanting a valve having at least one valve leaflet within the cardiovascular system of a subject. One step of the method includes preparing a substantially dehydrated bioprosthetic valve and then providing an expandable support member having oppositely disposed first and second ends and a main body portion extending between the ends. Next, the substantially dehydrated bioprosthetic valve is attached to the expandable support member so that the substantially dehydrated bioprosthetic valve is operably secured within the main body portion of the expandable support member. The expandable support member is then crimped into a compressed configuration and placed at a desired location within the cardiovascular system of the subject. Either before or after placement at the desired location, fluid or blood re-hydrates the substantially dehydrated bioprosthetic valve.

Abstract: One aspect of the present disclosure can include an intubating airway. The intubating airway can include a first component (14) having a first guiding surface and a second articulating component (16) that is attached to the first component via a hinge and has a second guiding surface. In a closed configuration, the first and second guiding surfaces can be flush with one another so that the first and second components collectively define a conduit having an interior passage that is dimensioned to direct a fiber-optic scope or an endotracheal tube extending through the interior passage for tracheal intubation. In an open configuration, the first and second guiding surfaces are not flush with one another so that no such conduit is formed.

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: An introducer sheath is provided for inserting at least one medical instrument directly toward a target patient tissue site. A plurality of lumens extend longitudinally between sheath proximal and distal ends of the introducer sheath. A septum extends between the sheath proximal and distal ends and selectively laterally separates each of the sheath lumens from one another. The septum is at least partially formed from a deformable and elastomeric material. The septum has a biased condition in which the plurality of sheath lumens are at least partially isolated from one another. The septum is selectively deflectable from the biased condition to at least partially place at least one of the plurality of sheath lumens in fluid communication with at least one other of the plurality of sheath lumens.

Abstract: A method of identifying an epileptogenic zone of a subjects brain includes: receiving a plurality N of physiological brain signals that extend over a duration, each of the plurality N of physiological brain signals acquired from the subjects brain; calculating within a time window a state transition matrix based on at least a portion of each of the plurality N of physiological brain signals, wherein the state transition matrix is a linear time invariant model of a network of N nodes corresponding to the plurality N of physiological brain signals; calculating a minimum norm of a perturbation on the state transition matrix that causes the network to transition from a stable state to an unstable state; and assigning a fragility metric to each of the plurality N of physiological brain signals based on the minimum norm of the perturbation for that physiological brain signal.