A deep learning based framework for the classification of multi- class capsule gastroscope image in gastroenterologic diagnosis.

Purpose: The purpose of this paper is to develop a method to automatic classify capsule gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such as atrophic gastritis (AG). The purpose of this research work is to develop a deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Method: In this research work, we proposed deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. We used VGG- 16, ResNet-50, and Inception V3 pre-trained models, fine-tuned them and adjust hyperparameters according to our classification problem. Results: A dataset containing 380 images was collected for each capsule gastroscope image category, and divided into training set and test set in a ratio of 70%, and 30% respectively, and then based on the dataset, three methods, including as VGG- 16, ResNet-50, and Inception v3 are used. We achieved highest accuracy of 94.80% by using VGG- 16 to diagnose and classify capsule gastroscopic images into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Our proposed approach classified capsule gastroscope image with respectable specificity and accuracy. Conclusion: The primary technique and industry standard for diagnosing and treating numerous stomach problems is gastroscopy. Capsule gastroscope is a new screening tool for gastric diseases. However, a number of elements, including image quality of capsule endoscopy, the doctors' experience and fatigue, limit its effectiveness. Early identification is necessary for high-risk factors for carcinogenesis, such as atrophic gastritis (AG). Our suggested framework will help prevent incorrect diagnoses brought on by low image quality, individual experience, and inadequate gastroscopy inspection coverage, among other factors. As a result, the suggested approach will raise the standard of gastroscopy. Deep learning has great potential in gastritis image classification for assisting with achieving accurate diagnoses after endoscopic procedures.

Dextran-based hydrogel formed by thiol-Michael addition reaction for 3D cell encapsulation.

Cell encapsulation in three-dimensional (3D) hydrogels can mimic native cell microenvironment and plays a major role in cell-based transplantation therapies. In this contribution, a novel in situ-forming hydrogel, Dex-l-DTT hydrogel ("l" means "linked-by"), by cross-linking glycidyl methacrylate derivatized dextran (Dex-GMA) and dithiothreitol (DTT) under physiological conditions, has been developed using thiol-Michael addition reaction. The mechanical properties, gelation process and degree of swelling of the hydrogel can be easily adjusted by changing the pH of phosphate buffer saline. The 3D cell encapsulation ability is demonstrated by encapsulating rat bone marrow mesenchymal stem cells (BMSCs) and NIH/3T3 fibroblasts into the in situ-forming hydrogel with maintained high viability. The BMSCs also maintain their differentiation potential after encapsulation. These results demonstrate that the Dex-l-DTT hydrogel holds great potential for biomedical field.