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The coronavirus disease 2019(COVID-19) pandemic poses a severe threat to global health.As an emerging infectious disease mainly attacking the respiratory tract,it has severely challenged the management of chronic non-infectious respiratory diseases including obstructive sleep apnea(OSA) and asthma.This article reviews the impact of OSA on the incidence of COVID-19 and the underlying pathophysiological mechanism,as well as the effects of OSA on the hospitalization risk and the prognosis and outcome of COVID-19 patients,which will provide novel ideas for the management of OSA during the COVID-19 pandemic.

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In addition to acute respiratory symptoms,coronavirus disease 2019(COVID-19)could cause olfactory dysfunction,which becomes the only clinical manifestation of COVID-19 in some cases.We review the epidemiological characteristics,pathological mechanism,screening value,treatment and prognosis of olfactory dysfunction in patients with COVID-19,aiming to achieve an in-depth understanding of the early diagnosis,quarantine,scientific treatment and prognosis of COVID-19.

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Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (L 1) and Ridge Regression (L 2). In recent years, researchers have proposed many other regularization terms. In theory, L q -type regularization has a lower q value, which means that it can be used to find solutions with better sparsity. L 1/2 regularization is of L q type (0 < q < 1) and has been shown to have many attractive properties. In this work, we studied the L 1/2 penalty in sparse logistic regression for three-classification EEG emotion recognition, and used a coordinate descent algorithm and a univariate semi-threshold operator to implement L 1/2 penalty logistic regression. The experimental results on simulation and real data demonstrate that our proposed method is better than other existing regularization methods. Sparse logistic regression with L 1/2 penalty achieves higher classification accuracy than the conventional L 1, Ridge Regression, and Elastic Net regularization methods, using fewer but more informative EEG signals. This is very important for high-dimensional small-sample EEG data and can help researchers to reduce computational complexity and improve computational accuracy. Therefore, we propose that sparse logistic regression with the L 1/2 penalty is an effective technique for emotion recognition in practical classification problems.