3D Printed Magneto‐Active Microfiber Scaffolds for Remote Stimulation and Guided Organization of 3D In Vitro Skeletal Muscle Models
Gerardo Cedillo‐Servin,
Ouafa Dahri,
João Meneses
et al.
Abstract:This work reports the rational design and fabrication of magneto‐active microfiber meshes with controlled hexagonal microstructures via melt electrowriting (MEW) of a magnetized polycaprolactone‐based composite. In situ iron oxide nanoparticle deposition on oxidized graphene yields homogeneously dispersed magnetic particles with sizes above 0.5 µm and low aspect ratio, preventing cellular internalization and toxicity. With these fillers, homogeneous magnetic composites with high magnetic content (up to 20 weig… Show more
“…Simultaneously, the hexagonal mesh can more compactly cover the image area, reducing redundancy. It can decrease edge effects when handling image boundaries, reducing the likelihood of extensive deformation at the image edges and thereby improving repre-sentation efficiency [60]. In contrast, rectangular meshes may require more mesh points to represent the same image area, resulting in larger input dimensions [61].…”
Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification.
“…Simultaneously, the hexagonal mesh can more compactly cover the image area, reducing redundancy. It can decrease edge effects when handling image boundaries, reducing the likelihood of extensive deformation at the image edges and thereby improving repre-sentation efficiency [60]. In contrast, rectangular meshes may require more mesh points to represent the same image area, resulting in larger input dimensions [61].…”
Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification.
“…Castilho and colleagues investigated magnetically active microfiber networks with controllable hexagonal microstructures based on iron-deposited oxidized graphene/poly(e-caprolactone) through melt extrusion. 380 Singlefiber elastic moduli reached 369 AE 20 MPa. Under cyclic magnetic fields with magnetic flux density B = 100 mT or higher, these skeletal muscle constructs immersed in culture medium successfully underwent reversible bending while maintaining the loaded cells, creating a three-dimensional cell culture environment.…”
This review critically analyzes degradable biomedical elastomers, focusing on their degradation, synthesis, microstructure, and role in tissue repair. It guides experts in balancing degradation with tissue repair for improved applications.
Melt electrowriting (MEW) is an emerging additive manufacturing (AM) technology that enables the precise deposition of continuous polymeric microfibers, allowing for the creation of high‐resolution constructs. In recent years, MEW has undergone a revolution, with the introduction of active properties or additional functionalities through novel polymer processing strategies, the incorporation of functional fillers, post‐processing, or the combination with other techniques. While extensively explored in biomedical applications, MEW's potential in other fields remains untapped. Thus, this review explores MEW's characteristics from a materials science perspective, emphasizing the diverse range of materials and composites processed by this technique and their current and potential applications. Additionally, the prospects offered by post‐printing processing techniques are explored, together with the synergy achieved by combining melt electrowriting with other manufacturing methods. By highlighting the untapped potentials of MEW, this review aims to inspire research groups across various fields to leverage this technology for innovative endeavors.This article is protected by copyright. All rights reserved
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