Abstract: A stabilizer for use with a delivery system is provided. The stabilizer receives a delivery system, such as a handle portion, and prevents unwanted movement of the delivery system during a medical procedure. The stabilizer desirably includes an elongate rail that is adapted to be secured to a base or table. A rail dock is adapted to releasably couple to the delivery system. The rail dock is slidably mounted along an upper surface of the rail and includes a brake assembly. The brake assembly preferably includes a toggle member with at least one push button, wherein actuation of the push button may release or lock the brake assembly. Components of the stabilizer are provided for allowing the stabilizer to be used with different delivery systems and the stabilizer is capable of being secured to different sized tables.

Abstract: Apparatuses, systems, and methods for prosthetic valves. An implantation site may comprise a native heart valve or another implantation site in examples. Examples may be utilized for improved anchoring and sealing of flow (e.g., paravalvular leakage) with a native heart valve having calcification. Examples may reduce the possibility of obstruction of a left ventricular outflow tract (LVOT) of a heart, whether resulting from implantation of a prosthetic valve or otherwise.

Abstract: A translatable electrode for use in a cartridge assembly for a contact start plasma arc torch including an electrode body having a longitudinal axis and including a proximal end and a distal end. The proximal end including a spiral groove and a contact surface at a proximal end face shaped to electrically communicate with a cathodic element. The translatable electrode also including at least one emissive insert disposed within the distal end of the electrode body and proximate a distal end face. The translatable electrode including at least one baffle disposed between the proximal and distal end of the electrode body. The translatable electrode also including a gas flow dampening region disposed circumferentially about the distal end and adjacent the distal end face and positioned between the at least one baffle and the distal end face.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: Examples of the present disclosure may be directed to reducing a tension force or a compression force in one or more shafts of the delivery system. An elongate catheter may include the one or more shafts. The tension force or compression force may result from residual tension or compression in a shaft resulting from movement of the shaft. The tension force or compression force may alternatively or in combination result from interacting forces across a plurality of the shafts. The tension force or compression force may hinder the operation of the delivery system and in instances may damage the delivery system. Systems, devices, and methods for reducing the tension force or compression force are disclosed herein.

Abstract: An implantable device includes one or more anchors. The anchors are configured to capture one or more leaflets of a native heart valve. The one or more anchors are configured to draw native valve leaflets into the device. The anchor portion can be extendable and retractable. The one or more anchors can be closed to secure the implantable device to the native valve leaflets.

Abstract: Devices, systems, and methods relate to implants, such as prosthetic valves for replacing native heart valves. Delivery systems for delivering the implants to an implantation site are also disclosed herein. A prosthetic valve may include a valve body supporting a plurality of prosthetic valve leaflets and one or more adjustable anchors coupled to the valve body for anchoring the prosthetic valve to leaflets of a native heart valve, wherein at least one of the adjustable anchors is axially slidable relative to the valve body for conforming to the native anatomy of the native heart valve.

Abstract: A prosthetic heart valve delivery system having one or more features which facilitate access to a target annulus and improve maneuverability. The system may have a flexible access sheath having an inner lumen and a proximal handle attached to an elongated flexible catheter extending distally therefrom and having an outer diameter sized to fit through the access sheath. An expandable prosthetic heart valve is crimped and positioned in an inner lumen and near the distal end of the delivery catheter. A distal tapered nose cone attaches to the delivery catheter and facilitates passage through the patient's vasculature. An inner tube extends from the proximal handle through the delivery catheter inner lumen, through the prosthetic heart valve, and attaches to the nose cone. Finally, the system has a guidewire that extends through the proximal handle along the delivery catheter and projects distally from the nose cone. The system is especially useful for transfemoral atrioventricular valve replacements.

Abstract: A graph may be initially seeded with nodes representing applications and tags describing subjective qualities of the applications. A system generates tags for new applications using a supervised machine learning model. The system extracts signals from a newly detected application. The system inputs the signals into a machine learning model, and receives, as output from the model, tags that correspond to the new application and levels of confidence for each tag. The system updates the graph to include one or more nodes corresponding to the new application, with the tags linked to the one or more nodes with an edge that has a weight corresponding to the level of confidence. The system receives a query corresponding to the tag and provides a response to the query based on the one or more nodes corresponding to the new application.

Abstract: A graph includes nodes representing applications and tags describing subjective qualities of the applications. The system responds to queries for user personas by using an LLM to match the persona to applications in the graph. The system receives a natural language query describing a persona. The system generates a prompt for an LLM based on the query and provides the prompt to the LLM for execution. The system receives, as output from the LLM, candidate applications. The system inputs the candidate applications into a classifier trained to classify candidate applications into known applications, applications that already exist in a graph. The system receives, as output from the classifier, known applications. The system determines, for each known application, a quality score of the known application and determines that the quality score exceeds a quality score threshold. In response, the system provides the known applications for display at a user interface.

Abstract: A graph includes nodes representing applications and tags describing subjective qualities of the applications. The system generates tags for applications using a large language model (LLM). The system receives the name of an application and generates a prompt for an LLM based on the name. The prompt includes a request for one or more tags associated with the application. The system provides the prompt to the LLM for execution and receives, as output from the LLM, candidate tags. The system inputs the candidate tags into a classifier trained to classify candidate tags into known tags, tags that already exist in a graph. The system receives, as output from the classifier, known tags. The system updates the graph to include a node corresponding to the application, the node linked to the known tags with one or more edges.

Abstract: This disclosure pertains to the field of nanotechnology. The disclosure provides methods of preparing nanostructures with fluoride passivation. The disclosure also provides methods of preparing nanostructures with fluoride and amine passivation. The nanostructures have high quantum yield, narrow emission peak width, tunable emission wavelength, and colloidal stability. Also provided are nanostructures prepared using the methods. And, nanostructure films and molded articles comprising the nanostructures are also provided.

Abstract: A steerable delivery system is provided for delivering a replacement heart valve to a native heart valve location. The steerable delivery system includes an outer sheath assembly having a capsule along a distal end portion for retaining the replacement heart valve in a compressed configuration. The capsule is constructed to provide both compression resistance and flexibility. The capsule may be formed with an outer polymer layer, a metal middle layer located on a radially inner surface of the outer polymer layer, and an inner liner located on a radially inner surface of the metal middle layer. A fluoroelastomer layer is provided for bonding the inner liner to the metal middle layer. The metal middle layer is preferably a metal hypotube.

Abstract: Devices, systems and methods are described herein to provide improved steerability for delivering a prosthesis to a body location, for example, for delivering a replacement mitral valve to a native mitral valve location. The steerable delivery system can contain a steerable rail configured for multi-plane bending to direct a distal end of the delivery system.

Abstract: A delivery system includes a suture attachment mechanism for maintaining a connection to a heart valve after initial deployment, thereby allowing recapture of the heart valve if desired. A plurality of suture portions is provided along a distal end portion of the delivery system, each suture portion having a first end fixed to the delivery system and a second end releasably coupled to a retention member on the delivery system. A knob on a handle of the delivery system is actuated for causing the second end of each suture portion to be released from its respective retention member, thereby allowing the suture portions to be decoupled from the heart valve. The suture portions are located only along a distal end portion of the delivery system for improving reliability and consistency.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: This disclosure pertains to the field of nanotechnology. The disclosure provides methods of preparing nanostructures with fluoride passivation. The disclosure also provides methods of preparing nanostructures with fluoride and amine passivation. The nanostructures have high quantum yield, narrow emission peak width, tunable emission wavelength, and colloidal stability. Also provided are nanostructures prepared using the methods. And, nanostructure films and molded articles comprising the nanostructures are also provided.

Abstract: Disclosed are embodiments of delivery systems for delivery of replacement heart valves. This can include mitral, aortic, tricuspid, and pulmonary valves. The delivery systems can include one or more different components and configurations that advantageously improve placement of the replacement heart valves during the operation of the delivery system.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.