Abstract: Vascularization is a critical component of tissue engineering research and is essential for enhancing the success rate of tissue construction and function. Over the past decade, researchers have explored various methods to construct in vitro vascular networks, including 3D printing, cell sphere technology, and microfluidics. Microfluidic technology has garnered significant attention due to its notable advantages in precision, controllability, flexibility, and applicability. It can be primarily classified into two modes: (i) the pre-designed mode, which involves creating vascular networks by pre-designing vascular channels and seeding endothelial cells, encompassing microfluidic chips and microfluidic spinning technologies; and (ii) the self-assembly mode, where cell spheres are f

Abstract: Effective vascularization is crucial for the success of tissue engineering and is influenced by numerous factors. The present work focuses on investigating the effect of a substance, cyanobacteria-loaded oxygen-releasing hydrogel, on vascularization and verifying the effect of photosynthetic-oxygen-releasing biomaterials containing a cyanobacteria hydrogel on angiogenesis, using the chick chorioallantoic membrane (CAM) as a model system. On the eighth day of embryonic development, cyanobacterial microspheres were placed on the CAM and maintained in a light incubator under appropriate growth and photosynthesis conditions. The effect of cyanobacterial microspheres on vascularization was evaluated from the eighth day of embryonic development. The carrier material used to prepare the

Abstract: Unsupervised Domain Adaptation aims to leverage a source domain with ample labeled data to tackle tasks on an unlabeled target domain. However, this poses a significant challenge, particularly in scenarios exhibiting significant disparities between the two domains. Prior methods often fall short in challenging domains due to the impact of incorrect pseudo-labeling noise and the limits of handcrafted domain alignment rules. In this paper, we propose a novel method called DCST (Dual Cross-Supervision Transformer), which improves upon existing methods in two key aspects. Firstly, vision transformer is combined with dual cross-supervision learning strategy to enforce consistency learning from different domains. The network accomplishes domain-specific self-training and cross-domain f

Abstract: The UNet architecture, based on convolutional neural networks (CNN), has demonstrated its remarkable performance in medical image analysis. However, it faces challenges in capturing long-range dependencies due to the limited receptive fields and inherent bias of convolutional operations. Recently, numerous transformer-based techniques have been incorporated into the UNet architecture to overcome this limitation by effectively capturing global feature correlations. However, the integration of the Transformer modules may result in the loss of local contextual information during the global feature fusion process. In this work, we propose a 2D medical image segmentation model called multi-scale cross perceptron attention network (MCPA). The MCPA consists of three main components: an