Objective To establish the rabbit model for acute retinal necrosis(ARN) and investigate the possible route of the virus migrates from one eye to the other. Design Experimental study. Participants 41 chinchilla rabbits. Methods HSV-1 COS strain was inoculated into subretina in 41 rabbits to establish ARN animal model. Ganciclovir was used to treat by intraocular and/or intravenous injection 1 day or 3 days after virus inoculation. PCR test of blood samples was performed for HSV detection. Histopathological examinations were also conducted in some eyeballs, optic nerves and brains of the rabbits. Main Outcome Measures Results of blood PCR and histopathology. Results 10 rabbits appeared bilateral retina necrosis (10 to 18 days after virus inoculation) and 15 rabbits appeared central nervous system damages. PCR samples of 5 rabbits obtained before and after the contralateral retina damages appeared (9 to 19 days after virus inoculation) had all negative results. Histopathological damages were found in bilateral eyeballs, optic nerves and brains. Conclusion The ARN model can be established by injection of HSV-1 into subretina of rabbits. Blood samples PCR don't support the hypothesis of virus transmission through circulation system in bilateral ARN cases. Trans-optic chiasm transmission may be a possible route.

Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional (3D) multi-view convolutional neural network (MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network (MVSECNN) model is constructed by introducing the squeeze-and-excitation (SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.
Humans ; Lung/pathology* ; Lung Neoplasms/pathology* ; Neural Networks, Computer ; Tomography, X-Ray Computed/methods*