Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.
In this study, an egg white dual cross-linked hydrogel was developed based on the principle that the external stimulus can denature proteins and cause them to aggregate, forming hydrogel. The sodium hydroxide was used to induce gelation of the egg white protein, subsequently introducing calcium ions to cross-link with protein chains, thereby producing a dual cross-linked hydrogel. The characteristics of the dual cross-linked hydrogels—including the secondary structure, stability, microstructure, swelling performance, texture properties, and biosafety—were investigated to determine the effects of calcium ion on the egg white hydrogel (EWG) and evaluate the potential application in the field of tissue engineering. Results showed that calcium ions could change the β-sheet content of the protein in EWG after soaking it in different concentrations of CaCl2 solution, leading to changes in the hydrogen bonds and the secondary structure of polypeptide chains. It was confirmed that calcium ions promoted the secondary cross-linking of the protein chain, which facilitated polypeptide folding and aggregation, resulting in enhanced stability of the egg white dual cross-linked hydrogel. Furthermore, the swelling capacity of the EWG decreased with increasing concentration of calcium ions, and the texture properties including hardness, cohesiveness and springiness of the hydrogels were improved. In addition, the calcium cross-linked EWG hydrogels exhibited biocompatibility and cell-surface adhesion in vitro. Hence, this work develops a versatile strategy to fabricate dual cross-linked protein hydrogel with biosafety and cell-surface adhesion, and both the strategy and calcium-egg white cross-linked hydrogels have potential for use in bone tissue engineering.
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