Background: Acute necrotizing encephalopathy (ANE) is a devastating neurologic condition that can arise following a variety of systemic infections, including influenza and SARS-Cov-2. The clinical features of COVID-19-associated ANE in pediatric patients based on multi-case data have not yet been described and remain obscure. We reviewed 12 pediatric patients to better describe the clinical features of ANE with COVID-19.Methods: We retrospectively collected and summarized the clinical features of ANE in children with COVID-19. Clinical data were collected from 12 children, including their general status, clinical symptoms, laboratory tests, and neuroimaging features.Results: Among the subjects, 10 were over 5 years old and they accounted for 83.33%. A large percentage of those affected (66.67%) were females. The major manifestations included fever (100%), impaired consciousness (100%), and convulsions (75%). We determined that increased interleukin (IL)-6 and IL-10, and tumor necrosis factor-α and interferon gamma were not predictive of severe ANE and mortality in children with COVID-19 in this study. All children presented with abnormal neuroimaging with multiple and symmetrically distributed lesions, involving the thalamus, basal ganglia, cerebellum, and brain hemispheres. Eight of the 12 children died, resulting in a mortality rate of 66.67%, and 75% of these children were females. Importantly, we found the timely administration of mannitol after an acute onset of convulsions or disturbance of consciousness may be decreased the high mortality induced by ANE children with COVID-19.Conclusion: COVID-19 associated with ANE in children is characterized by sudden symptom onset, rapid disease progression, and high mortality.

Medical images and data may encounter tampering, compromising patient's privacy and other safety issues in the process of storage and transmission of information. This paper proposes an algorithm of zero watermarking to address these issues using DFT. The algorithm avoids the sophisticated process of finding the Region of Non-Interest (RONI) of medical images, and uses a part of sign sequence of DFT coefficients as the feature vector of images for enhancing the robustness. Even if the algorithm is public, it can still withstand malicious attacks by using the private key to protect the medical image. Experimental result indicates that the watermarking scheme belongs to the blind watermarking extraction with strong robustness and invisibility to withstand rotation, scaling, translation, cropping and other attacks. Moreover, it can embed much more data compared with the existing watermarking techniques.

It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor’s opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases.