Objective To study effect of virus inactivation/removal treated by solvent/detergent method and dry heating at 80℃, 72 h for inactivation in human coagulation factor Ⅷ.Methods Human coagulation factor Ⅷextracted from healthy human plas-ma were treated by solvent/detergent method and dry heating at 80℃, 72 h for inactivation .The virus inactivation effect was validated by adding the indicator virus ( PRV, Sindbis, HIV, EMCV, PPV).Results The methods could effectively inactivate lipid-enveloped and non lipid-enveloped viruses which could be used for virus inactivation /removal during human coagulation factor Ⅷexperiments , the residual amount of TNBP in production was less than one percent ten thousand (10 ppm), the residual Tween-80 concentration was less than one percent hundred thousand (100 ppm),which all met the safety standards .Conclusion and no significant change was ob-served in the activation and other indicators of human coagulation factor Ⅷ.

Traditional biomedical data analysis technology faces enormous challenges in the context of the big data era. The application of deep learning technology in the field of biomedical analysis has ushered in tremendous development opportunities. In this paper, we reviewed the latest research progress of deep learning in the field of biomedical data analysis. Firstly, we introduced the deep learning method and its common framework. Then, focusing on the proposal of biomedical problems, data preprocessing method, model building method and training algorithm, we summarized the specific application of deep learning in biomedical data analysis in the past five years according to the chronological order, and emphasized the application of deep learning in medical assistant diagnosis. Finally, we gave the possible development direction of deep learning in the field of biomedical data analysis in the future.
Algorithms ; Biomedical Technology ; Data Analysis ; Deep Learning