A thrombin-triggered self-regulating anticoagulant strategy combined with anti-inflammatory capacity for blood-contacting implants

Interrelated coagulation and inflammation are impediments to endothelialization, a prerequisite for the longterm function of cardiovascular materials. Here, we proposed a self-regulating anticoagulant coating strategy combined with anti-inflammatory capacity, which consisted of thrombin-responsive nanogels with anticoagulant and anti-inflammatory components. As an anticoagulant, rivaroxaban was encapsulated in nanogels cross-linked by thrombin-cleavable peptide and released upon the trigger of environmental thrombin, blocking the further coagulation cascade. The superoxide dismutase mimetic Tempol imparted the antioxidant property. Polyphenol epigallocatechin gallate (EGCG), in addition to its anti-inflammatory function in synergy with Tempol, also acted as a weak cross-linker to stabilize the coating. The effectiveness and versatility of this coating were validated using two typical cardiovascular devices as models, biological valves and vascular stents. It was demonstrated that the coating worked as a precise strategy to resist coagulation and inflammation, escorted reendothelialization on the cardiovascular devices, and provided a new perspective for designing endothelium-like functional coatings

Platelet Membrane-Coated Nanocarriers Targeting Plaques to Deliver Anti-CD47 Antibody for Atherosclerotic Therapy

Atherosclerosis, the principle cause of cardiovascular disease (CVD) worldwide, is mainly characterized by the pathological accumulation of diseased vascular cells and apoptotic cellular debris. Atherogenesis is associated with the upregulation of CD47, a key antiphagocytic molecule that is known to render malignant cells resistant to programmed cell removal, or "efferocytosis." Here, we have developed platelet membrane-coated mesoporous silicon nanoparticles (PMSN) as a drug delivery system to target atherosclerotic plaques with the delivery of an anti-CD47 antibody. Briefly, the cell membrane coat prolonged the circulation of the particles by evading the immune recognition and provided an affinity to plaques and atherosclerotic sites. The anti-CD47 antibody then normalized the clearance of diseased vascular tissue and further ameliorated atherosclerosis by blocking CD47. In an atherosclerosis model established in ApoE-/- mice, PMSN encapsulating anti-CD47 antibody delivery significantly promoted the efferocytosis of necrotic cells in plaques. Clearing the necrotic cells greatly reduced the atherosclerotic plaque area and stabilized the plaques reducing the risk of plaque rupture and advanced thrombosis. Overall, this study demonstrated the therapeutic advantages of PMSN encapsulating anti-CD47 antibodies for atherosclerosis therapy, which holds considerable promise as a new targeted drug delivery platform for efficient therapy of atherosclerosis

Photo-functionalized TiO2 nanotubes decorated with multifunctional Ag nanoparticles for enhanced vascular biocompatibility

Titanium dioxide (TiO2) has a long history of application in blood contact materials, but it often suffers from insufficient anticoagulant properties. Recently, we have revealed the photocatalytic effect of TiO2 also induces anticoagulant properties. However, for long-term vascular implant devices such as vascular stents, besides anticoagulation, also anti-inflammatory, anti-hyperplastic properties, and the ability to support endothelial repair, are desired. To meet these requirements, here, we immobilized silver nanoparticles (AgNPs) on the surface of TiO2 nanotubes (TiO2-NTs) to obtain a composite material with enhanced photo-induced anticoagulant property and improvement of the other requested properties. The photo-functionalized TiO2-NTs showed protein-fouling resistance, causing the anticoagulant property and the ability to suppress cell adhesion. The immobilized AgNPs increased the photocatalytic activity of TiO2-NTs to enhances its photo-induced anticoagulant property. The AgNP density was optimized to endow the TiO2-NTs with anti-inflammatory property, a strong inhibitory effect on smooth muscle cells (SMCs), and low toxicity to endothelial cells (ECs). The in vivo test indicated that the photofunctionalized composite material achieved outstanding biocompatibility in vasculature via the synergy of photo-functionalized TiO2-NTs and the multifunctional AgNPs, and therefore has enormous potential in the field of cardiovascular implant devices. Our research could be a useful reference for further designing of multifunctional TiO2 materials with high vascular biocompatibility

A review of the development of interventional devices for mitral valve repair with the implantation of artificial chords

Mitral regurgitation (MR) was the most common heart valve disease. Surgical repair with artificial chordal replacement had become one of the standard treatments for mitral regurgitation. Expanded polytetrafluoroethylene (ePTFE) was currently the most commonly used artificial chordae material due to its unique physicochemical and biocompatible properties. Interventional artificial chordal implantation techniques had emerged as an alternative treatment option for physicians and patients in treating mitral regurgitation. Using either a transapical or a transcatheter approach with interventional devices, a chordal replacement could be performed transcatheter in the beating heart without cardiopulmonary bypass, and the acute effect on the resolution of mitral regurgitation could be monitored in real-time by transesophageal echo imaging during the procedure. Despite the in vitro durability of the expanded polytetrafluoroethylene material, artificial chordal rupture occasionally occurred. In this article, we reviewed the development and therapeutic results of interventional devices for chordal implantation and discuss the possible clinical factors responsible for the rupture of the artificial chordal material

Towards prediction of ordered phases in rechargeable battery chemistry via group–subgroup transformation

Abstract: The electrochemical thermodynamic and kinetic characteristics of rechargeable batteries are critically influenced by the ordering of mobile ions in electrodes or solid electrolytes. However, because of the experimental difficulty of capturing the lighter migration ion coupled with the theoretical limitation of searching for ordered phases in a constrained cell, predicting stable ordered phases involving cell transformations or at extremely dilute concentrations remains challenging. Here, a group-subgroup transformation method based on lattice transformation and Wyckoff-position splitting is employed to predict the ordered ground states. We reproduce the previously reported Li0.75CoO2, Li0.8333CoO2, and Li0.8571CoO2 phases and report a new Li0.875CoO2 ground state. Taking the advantage of Wyckoff-position splitting in reducing the number of configurations, we identify the stablest Li0.0625C6 dilute phase in Li-ion intercalated graphite. We also resolve the Li/La/vacancy ordering in Li3xLa2/3−xTiO3 (0 < x < 0.167), which explains the observed Li-ion diffusion anisotropy. These findings provide important insight towards understanding the rechargeable battery chemistry

CF-YOLOX: An Autonomous Driving Detection Model for Multi-Scale Object Detection

In self-driving cars, object detection algorithms are becoming increasingly important, and the accurate and fast recognition of objects is critical to realize autonomous driving. The existing detection algorithms are not ideal for the detection of small objects. This paper proposes a YOLOX-based network model for multi-scale object detection tasks in complex scenes. This method adds a CBAM-G module to the backbone of the original network, which performs grouping operations on CBAM. It changes the height and width of the convolution kernel of the spatial attention module to 7 × 1 to improve the ability of the model to extract prominent features. We proposed an object-contextual feature fusion module, which can provide more semantic information and improve the perception of multi-scale objects. Finally, we considered the problem of fewer samples and less loss of small objects and introduced a scaling factor that could increase the loss of small objects to improve the detection ability of small objects. We validated the effectiveness of the proposed method on the KITTI dataset, and the mAP value was 2.46% higher than the original model. Experimental comparisons showed that our model achieved superior detection performance compared to other models

Monocular Depth Estimation Algorithm Integrating Parallel Transformer and Multi-Scale Features

In the process of environmental perception, traditional CNN is often unable to effectively capture global context information due to its network structure, which leads to the problem of blurred edges of objects and scenes. Aiming at this problem, a self-supervised monocular depth estimation algorithm incorporating a Transformer is proposed. First of all, the encoder-decoder architecture is adopted. In the course of the encoding procedure, the input image generates images with different patch sizes but the same size. The multi-path Transformer network and single-path CNN network are used to extract global and local features, respectively, and feature fusion is achieved through interactive modules, which improves the network’s ability to acquire global information. Second, a multi-scale fusion structure of hierarchical features is designed to improve the utilization of features of different scales. Experiments for training the model were conducted using the KITTI dataset. The outcomes reveal that the proposed algorithm outperforms the mainstream algorithm. Compared with the latest CNN-Transformer algorithm, the proposed algorithm reduces the absolute relative error by 3.7% and the squared relative error by 3.9%