Class-Aware Attention Network for infectious keratitis diagnosis using corneal photographs.

Infectious keratitis is one of the common ophthalmic diseases and also one of the main blinding eye diseases in China, hence rapid and accurate diagnosis and treatment for infectious keratitis are urgent to prevent the progression of the disease and limit the degree of corneal injury. Unfortunately, the traditional manual diagnosis accuracy is usually unsatisfactory due to the indistinguishable visual features. In this paper, we propose a novel end-to-end fully convolutional network, named Class-Aware Attention Network (CAA-Net), for automatically diagnosing infectious keratitis (normal, viral keratitis, fungal keratitis, and bacterial keratitis) using corneal photographs. In CAA-Net, a class-aware classification module is first trained to learn class-related discriminative features using separate branches for each class. Then, the learned class-aware discriminative features are fed into the main branch and fused with other feature maps using two attention strategies to assist the final multi-class classification performance. For the experiments, we have built a new corneal photograph dataset with 1886 images from 519 patients and conducted comprehensive experiments to verify the effectiveness of our proposed method. The code is available at https://github.com/SWF-hao/CAA-Net_Pytorch.

The long-term consequences of Corona Virus Disease 2019 patients receiving Chinese herbal medicine treatments in acute phase: A protocol for systematic review and meta-analysis.

BACKGROUND: In December 2019, the first case of Corona Virus Disease 2019 (COVID-19) associated with severe acute respiratory syndrome coronavirus-2 viral infection was described in Wuhan. Similar to SARS in 2003, COVID-19 also had a lasting impact. Approximately 76% of patients discharged after hospitalization for COVID-19 had neurological manifestations which could persist for 6 months, and some long-term consequences such as the gradual loss of lung function due to pulmonary interstitial fibrosis could have comprehensive effects on daily quality of life for people who were initially believed to have recovered from COVID-19. METHODS AND ANALYSIS: Our comprehensive search strategy developed in consultation with a research librarian. We will search these following electronic databases: PubMed, Cochrane Library, Web of Science, ScienceDirect, Scopus, Google Scholar, Embase, ProQuest, China Science and Technology Journal Database (VIP), China National Knowledge Infrastructure, WANFANG DATA, WHO covid-19 website, and Centers for Disease Control and the Prevention COVID-19 websites of the United States and China. The bias of publication will be confirmed via the P value of Egger test. The quality of studies will be evaluated by the Newcastle-Ottawa Scale. ETHICS AND DISSEMINATION: There are no ethical considerations associated with this study protocol for this systematic review which mainly focuses on the examination of secondary data. On completion of this analysis, we will prepare a manuscript for publication in a peer-reviewed medical journal. PROSPERO REGISTRATION NUMBER: CRD42021258711.

The Spring Festival Effect: The change in NO2 column concentration in China caused by the migration of human activities

The Spring Festival is the most important holiday in China, and human activity and population mobility may contribute greatly to air quality. According to the satellite-based tropospheric nitrogen dioxide (NO2) column and ground-based observational concentration of NO2 in megacities from 2013 to 2018 around the Spring Festival, we found that NO2 concentration obviously decreases, presenting a “tide phenomenon”, particularly in the megacities, with the tropospheric NO2 column density decreasing by 31.8%–44.5%. The tropospheric NO2 column density in Beijing decreased by 41.6% and rebounded by 22.3% after the festival. Vehicle sources were among the important causes of NOx emissions in the megacities, and traffic intensity decreased significantly during the festival. As the coronavirus disease 2019 (COVID-19) pandemic progresses, the traffic intensity in urban areas is decreasing significantly, with the tropospheric NO2 column density decreasing by 56.2% and rebounding by only 6.8% in 2020, without the “tide phenomenon”.