, in Wuhan, China. There are over 1,800,000 confirmed cases worldwide. 1 The pathological process of severe COVID-19 pneumonia is an inflammation reaction characterized by the destruction of the deep airway and alveolar. 2 It is currently considered that lung injury is not only associated with the direct virus-induced damage, but also the immune responses triggered by COVID-19 that lead to the activation of immune cells to release a large number of pro-and anti-inflammatory cytokines. Histologic examination has shown diffuse alveolar damage and mucinous exudate, which is similar to acute respiratory distress syndrome. 2 Aggravation of symptoms always occurs during 5-7 days after onset in patients with COVID-19 pneumonia and severe cases develop rapidly to acute respiratory failure. 3 Therefore, it is important to strengthen the treatment to suppress the proinflammatory response and control the cytokine storm at this stage. Methylprednisolone are the classical immunosuppressive drugs, which are important to stop or delay the progress of the pneumonia, and have been proved to be effective for the treatment of acute respiratory distress syndrome (ARDS). In a recent study, Wu et al. 4 found the administration of methylprednisolone appeared to reduce the risk of death in COVID-19 pneumonia patients with ARDS, however, of those who received methylprednisolone treatment, 23 of 50 patients died. This is a rather high mortality rate of~50%; therefore, in terms of the indication, timing, dosage and duration, the application of methylprednisolone warrants further investigation. In another study, Zhou et al. 5 endorsed the potential benefits of low-dose corticosteroids treatment in a subset of critically ill patients with COVID-19 pneumonia, however, the data was limited to only 15 patients and no control group. Although this is an important issue with regard to the challenges in the treatment of severe COVID-19 pneumonia, the clinical applicability of methylprednisolone needs to be tempered owing to the unanswered questions that remain. To address this issue, we performed a retrospective cohort study comparing the clinical outcomes of COVID-19 pneumonia patients with or without methylprednisolone treatment. We studied 46 severe patients with COVID-19 pneumonia at the

Background: Severe patients with 2019 novel coronavirus (2019-nCoV) pneumonia progressed rapidly to acute respiratory failure. We aimed to evaluate the definite efficacy and safety of corticosteroid in the treatment of severe COVID-19 pneumonia.Methods: Forty-six hospitalized patients with severe COVID-19 pneumonia hospitalized at Wuhan Union Hospital from January 20 to February 25, 2020, were retrospectively reviewed.The patients were divided into two groups based on whether they received corticosteroid treatment. The clinical symptoms and chest computed tomography(CT) results were compared.Results: A total of 26 patients received intravenous administration of methylprednisolone with a dosage of 1-2mg/kg/d for 5-7 days, while the remaining patients not. There was no significant

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the low-rank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method only needs to update two smaller tensors, which can be more efficiently conducted than computing t-SVD. Furthermore, we prove that the proposed alternating minimization algorithm can converge to a Karush-Kuhn-Tucker point. Experimental results on the synthetic data recovery, image and video inpainting tasks clearly demonstrate the superior performance and efficiency of our developed method over state-of-the-arts including the TNN and matricization methods.