[Protective effects of da chai hu granules (DCHKL) against alloxan (AXN)-induced rat pancreatic islets damage].

The protective effects of Da Chai Hu Granules (DCHKL) on islet cells which were incubated with 4 mmol x L(-1) alloxan (AXN) were studied. The viability of islet cells were measured with MTT. Insulin released into medium and in islets was detected by radioimmunoassay. Cell apoptosis rate was determined by flow cytometry. The expression of anti-apoptotic gene Bcl-2 and pro-apoptotic gene Bax in islet cells were measured with RT-PCR (reverse transcription polymerase chain reaction). Serum containing DCHKL can promote the activity of islet cells significantly (P < 0.01). Basal insulin secretion and high glucose-stimulated insulin secretion increased significantly (P < 0.01). Serum containing DCHKL can inhibit apoptosis of islet cells, the ratio of apoptosis was decreased. Serum containing DCHKL increased expression of Bcl-2 mRNA and decreased expression of Bax mRNA. DCHKL can significantly promote proliferation of islet cells and increase the amount of basal secretion of pancreatic islet cells and high glucose-stimulated insulin secretion. The expression of Bcl-2 increased significantly. The expression of Bax decreased significantly. DCHKL have a protective effect on the islet cells.

Deep Learning Technology Applied to Medical Image Tissue Classification.

Medical image classification is a novel technology that presents a new challenge. It is essential that pathological images are automatically and correctly classified to enable doctors to provide precise treatment. Convolutional neural networks have demonstrated their effectiveness in classifying images in deep learning, which may have dozens or hundreds of layers, to illustrate the relationship between them in terms of their different neural network features. Convolutional layers consisting of small kernels take weights as input and guide them through an activation function as output. The main advantage of using convolutional neural networks (CNNs) instead of traditional neural networks is that they reduce the model parameters for greater accuracy. However, many studies have simply been focused on finding the best CNN model and classification results from a single medical image classification. Therefore, we applied a common deep learning network model in an attempt to identify the best model framework by training and validating different model parameters to classify medical images. After conducting experiments on six publicly available databases of pathological images, including colorectal cancer tissue, chest X-rays, common skin lesions, diabetic retinopathy, pediatric chest X-ray, and breast ultrasound image datasets, we were able to confirm that the recognition accuracy of the Inception V3 method was significantly better than that of other existing deep learning models.