The coronavirus disease 2019 (COVID-19), a pneumonia caused by a novel coronavirus, was reported in December 2019. COVID-19 is highly contagious and has rapidly developed from a regional epidemic into a global pandemic. As yet, no effective drugs have been found to treat this virus. This study, an ongoing multicenter and blind randomized controlled trial (RCT), is being conducted at ten study sites in Heilongjiang Province, China, to investigate the efficacy and safety of Triazavirin (TZV) versus its placebo in COVID-19 patients. A total of 240 participants with COVID-19 are scheduled to be enrolled in this trial. Participants with positive tests of throat swab virus nucleic acid are randomized (11) into two groups standard therapy plus TZV or standard therapy plus placebo for a 7-day treatment with a 21-day follow-up. The primary outcome is the time to clinical improvement of the subjects. Secondary outcomes include clinical improvement rate, time to alleviation of fever, mean time and proportion of obvious inflammatory absorption in the lung, conversion rate of repeated negative virus nucleic acid tests, mortality rate, and conversion rate to severe and critically severe patients. Adverse events, serious adverse events, liver function, kidney function, and concurrent treatments will be monitored and recorded throughout the trial. The results of this trial should provide evidence-based recommendations to clinicians for the treatment of COVID-19.The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.Masks have become one of the most indispensable pieces of personal protective equipment and are important strategic products during the coronavirus disease 2019 (COVID-19) pandemic. Due to the huge mask demand-supply gap all over the world, the development of user-friendly technologies and methods is urgently needed to effectively extend the service time of masks. In this article, we report a very simple approach for the decontamination of masks for multiple reuse during the COVID-19 pandemic. Used masks were soaked in hot water at a temperature greater than 56 °C for 30 min, based on a recommended method to kill COVID-19 virus by the National Health Commission of the People’s Republic of China. The masks were then dried using an ordinary household hair dryer to recharge the masks with electrostatic charge to recover their filtration function (the so-called “hot water decontamination + charge regeneration” method). Three kinds of typical masks (disposable medical masks, surgical masks, and KN95-grade masks) were treated and tested. The filtration efficiencies of the regenerated masks were almost maintained and met the requirements of the respective standards. These findings should have important implications for the reuse of polypropylene masks during the COVID-19 pandemic. The performance evolution of masks during human wear was further studied, and a company (Zhejiang Runtu Co., Ltd.) applied this method to enable their workers to extend the use of masks. Mask use at the company was reduced from one mask per day per person to one mask every three days per person, and 122 500 masks were saved during the period from 20 February to 30 March 2020. Furthermore, a new method for detection of faulty masks based on the penetrant inspection of fluorescent nanoparticles was established, which may provide scientific guidance and technical methods for the future development of reusable masks, structural optimization, and the formulation of comprehensive performance evaluation standards.Diabetes and its related metabolic disorders have been reported as the leading comorbidities in patients with coronavirus disease 2019 (COVID-19). This clinical study aims to investigate the clinical features, radiographic and laboratory tests, complications, treatments, and clinical outcomes in COVID-19 patients with or without diabetes. This retrospective study included 208 hospitalized patients (≥ 45 years old) with laboratory-confirmed COVID-19 during the period between 12 January and 25 March 2020. Information from the medical record, including clinical features, radiographic and laboratory tests, complications, treatments, and clinical outcomes, were extracted for the analysis. 96 (46.2%) patients had comorbidity with type 2 diabetes. In COVID-19 patients with type 2 diabetes, the coexistence of hypertension (58.3% vs 31.2%), coronary heart disease (17.1% vs 8.0%), and chronic kidney diseases (6.2% vs 0%) was significantly higher than in COVID-19 patients without type 2 diabetes. KRIBB11 in vivo The frequency and degree of abnormalities in computed tomography (CT) chest scans in COVID-19 patients with type 2 diabetes were markedly increased, including ground-glass opacity (85.