Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring.

Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.

Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning.

Objective: To overcome limitations of open surgery artificial intelligence (AI) models by curating the largest collection of annotated videos and to leverage this AI-ready data set to develop a generalizable multitask AI model capable of real-time understanding of clinically significant surgical behaviors in prospectively collected real-world surgical videos. Design, Setting, and Participants: The study team programmatically queried open surgery procedures on YouTube and manually annotated selected videos to create the AI-ready data set used to train a multitask AI model for 2 proof-of-concept studies, one generating surgical signatures that define the patterns of a given procedure and the other identifying kinematics of hand motion that correlate with surgeon skill level and experience. The Annotated Videos of Open Surgery (AVOS) data set includes 1997 videos from 23 open-surgical procedure types uploaded to YouTube from 50 countries over the last 15 years. Prospectively recorded surgical videos were collected from a single tertiary care academic medical center. Deidentified videos were recorded of surgeons performing open surgical procedures and analyzed for correlation with surgical training. Exposures: The multitask AI model was trained on the AI-ready video data set and then retrospectively applied to the prospectively collected video data set. Main Outcomes and Measures: Analysis of open surgical videos in near real-time, performance on AI-ready and prospectively collected videos, and quantification of surgeon skill. Results: Using the AI-ready data set, the study team developed a multitask AI model capable of real-time understanding of surgical behaviors-the building blocks of procedural flow and surgeon skill-across space and time. Through principal component analysis, a single compound skill feature was identified, composed of a linear combination of kinematic hand attributes. This feature was a significant discriminator between experienced surgeons and surgical trainees across 101 prospectively collected surgical videos of 14 operators. For each unit increase in the compound feature value, the odds of the operator being an experienced surgeon were 3.6 times higher (95% CI, 1.67-7.62; P = .001). Conclusions and Relevance: In this observational study, the AVOS-trained model was applied to analyze prospectively collected open surgical videos and identify kinematic descriptors of surgical skill related to efficiency of hand motion. The ability to provide AI-deduced insights into surgical structure and skill is valuable in optimizing surgical skill acquisition and ultimately improving surgical care.

Synthesis, characterization, and humidity-responsiveness of guar gum xanthate and its nanocomposite with copper sulfide covellite.

A novel conjugation of guar gum with xanthate groups via facile aqueous xanthation reaction has been reported. Density of grafted xanthate on guar gum product (GG-X) is as high as 4.4%, thus GG-X is conceivably characterized and confirmed by various spectrometric, electrochemical, thermogravimetric, and microscopic methods. Complexation of GG-X with numerous borderline and soft metal ions (e.g. Fe2+, Co2+, Ni2+, Cu2+, Pb2+, Pt2+ and Cd2+) yields hydrophilic gel-like materials and shows good agreement with hard and soft acid and base (HSAB) theory. This indicates tremendous potential of GG-X in metal ion extraction, removal and hydrogel cross-linking. GG-X is also employed to formulate an aqueous colloidal dispersion of copper sulfide covellite (GG-X/CuS) nanocomposites. GG-X therefore behaves as a surfactant, allowing formation of electronically conductive nanocomposites. XRD indicates apparent beneficial effects of GG-X in the synthesis of CuS with a crystallite size of 15.6 nm. This novel nanocomposite is a promising material for humidity sensing, showing reversible linear responses to relative humidity changes within 10 to 80% range. The interaction between GG-X and water might cause changes in electrical permittivity of GG-X/CuS nanocomposite and/or electrical hopping conductivity between CuS nanoparticles.

Self-Healing Soft Sensors: From Material Design to Implementation.

The demand for interfacing electronics in everyday life is rapidly accelerating, with an ever-growing number of applications in wearable electronics and electronic skins for robotics, prosthetics, and other purposes. Soft sensors that efficiently detect environmental or biological/physiological stimuli have been extensively studied due to their essential role in creating the necessary interfaces for these applications. Unfortunately, due to their natural softness, these sensors are highly sensitive to structural and mechanical damage. The integration of natural properties, such as self-healing, into these systems should improve their reliability, stability, and long-term performance. Recent studies on self-healing soft sensors for varying chemical and physical parameters are herein reviewed. In addition, contemporary studies on material design, device structure, and fabrication methods for sensing platforms are also discussed. Finally, the main challenges and future perspectives in this field are introduced, while focusing on the most promising examples and directions already reported.

A Multifunctional Electronic Skin Empowered with Damage Mapping and Autonomic Acceleration of Self-Healing in Designated Locations.

Integrating self-healing capabilities into soft electronic devices and sensors is important for increasing their reliability, longevity, and sustainability. Although some advances in self-healing soft electronics have been made, many challenges have been hindering their integration in digital electronics and their use in real-world conditions. Herein, an electronic skin (e-skin) with high sensing performance toward temperature, pressure, and pH levels-both at ambient and/or in underwater conditions is reported. The e-skin is empowered with a novel self-repair capability that consists of an intrinsic mechanism for efficient self-healing of small-scale damages as well as an extrinsic mechanism for damage mapping and on-demand self-healing of big-scale damages in designated locations. The overall design is based on a multilayered structure that integrates a neuron-like nanostructured network for self-monitoring and damage detection and an array of electrical heaters for selective self-repair. This system has significantly enhanced self-healing capabilities; for example, it can decrease the healing time of microscratches from 24 h to 30 s. The electronic platform lays down the foundation for the development of a new subcategory of self-healing devices in which electronic circuit design is used for self-monitoring, healing, and restoring proper device function.

Angular compounding for speckle reduction in optical coherence tomography using geometric image registration algorithm and digital focusing.

Optical coherence tomography (OCT) suffers from speckle noise due to the high spatial coherence of the utilized light source, leading to significant reductions in image quality and diagnostic capabilities. In the past, angular compounding techniques have been applied to suppress speckle noise. However, existing image registration methods usually guarantee pure angular compounding only within a relatively small field of view in the focal region, but produce spatial averaging in the other regions, resulting in resolution loss and image blur. This work develops an image registration model to correctly localize the real-space location of every pixel in an OCT image, for all depths. The registered images captured at different angles are fused into a speckle-reduced composite image. Digital focusing, based on the convolution of the complex OCT images and the conjugate of the point spread function (PSF), is studied to further enhance lateral resolution and contrast. As demonstrated by experiments, angular compounding with our improved image registration techniques and digital focusing, can effectively suppress speckle noise, enhance resolution and contrast, and reveal fine structures in ex-vivo imaged tissue.