With redox-sensitive fluorescene probes DCFH-DA and DHR123, the formation of cytosolic and intramitochondrial reactive oxygen species (ROS) inside immature rat cerebellar granule cells during the apoptosis induced by nitric oxide donor S-nitroso-N-acetyl-pennicillamine (SNAP) was monitored by laser confocal scanning microscopy. The cytosolic and intramitochondrial ROS increase significantly after 0.5 mmol/L SNAP treatment for 1 h. Pre-treatment with the nitric oxide scavenger hemoglobin can effectively inhibit the formation of cytosolic and intrarnitochondrial ROS and protect neurons from apoptosis. Adding glutathione can also protect neurons from apoptosis, and the cytotoxity of nitric oxide increases significantly while the synthesis of glutathione is inhibited. The results indicated that ROS might be involved in NO-induced apoptosis in neural cells and glutathione might be the endogenesis antioxidant to protect neurons from oxidative injury.

Objective:To explore a blood cell recognition algorithm that combined threshold image segmentation with deep learning in conventional image processing,so as to be used in automatic recognition and classification of blood cell smears.Method:Global threshold segmentation was used to extract blood cells from blood cell smears and to store them separately.The segmented cell images were manually labeled and classified so as to reduce the requirements for hardware in subsequent processing.The deep learning training of labeled images was on the basis of the GoogLeNet pre training model,which could generate deep learning model of automatic recognition that could be used in the images of blood cell smear.The trained model could be used to evaluate the test set,and generate confusion matrix and area under curve(AUC)value of receiver operating characteristic(ROC)curve.Result:This preprocessing has been proven that it can improve the training of deep learning model,and the subsequent recognition speed of using model can exceed over 10 times.Using the online image dataset Raabin WBC Data of blood cell smear,the accuracy of model training reached to 93.06%.Both of them obtained favorable results.Conclusion:The blood cell recognition algorithm based on the combination of threshold image segmentation and deep learning can greatly improve the efficiency of recognition and classification of blood cells,and ensure accuracy of the diagnosis of blood related diseases.