Comparative efficacy evaluation of disinfectants against severe acute respiratory syndrome coronavirus-2.

BACKGROUND: Disinfection is one of the most effective ways to block the rapid transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Due to the prolonged coronavirus disease 2019 (COVID-19) pandemic, disinfectants have become crucial to prevent person-to-person transmission and decontaminate hands, clothes, facilities and equipment. However, there is a lack of accurate information on the virucidal activity of commercial disinfectants. AIM: To evaluate the virucidal efficacy of 72 commercially available disinfectants constituting 16 types of ingredients against SARS-CoV-2. METHODS: SARS-CoV-2 was tested with various concentrations of disinfectants at indicated exposure time points as recommended by the manufacturers. The 50% tissue culture infectious dose assay was used to calculate virus titre, and trypan blue staining and CCK-8 were used to assess cell viability after 3-5 days of SARS-CoV-2 infection. FINDINGS: This study found that disinfectants based on 83% ethanol, 60% propanol/ethanol, 0.00108-0.0011% sodium dichloroisocyanurate and 0.497% potassium peroxymonosulfate inactivated SARS-CoV-2 effectively and safely. Although disinfectants based on 0.05-0.4% benzalkonium chloride (BAC), 0.02-0.07% quaternary ammonium compound (QAC; 1:1), 0.4% BAC/didecyldimethylammonium chloride (DDAC), 0.28% benzethonium chloride concentrate/2-propanol, 0.0205-0.14% DDAC/polyhexamethylene biguanide hydrochloride (PHMB) and 0.5% hydrogen peroxide inactivated SARS-CoV-2 effectively, they exhibited cytotoxicity. Conversely, disinfectants based on 0.04-4% QAC (2:3), 0.00625% BAC/DDAC/PHMB, and 0.0205-0.14% and 0.0173% peracetic acid showed approximately 50% virucidal efficacy with no cytotoxicity. Citric acid (0.4%) did not inactivate SARS-CoV-2. CONCLUSION: These results indicate that most commercially available disinfectants exert a disinfectant effect against SARS-CoV-2. However, re-evaluation of the effective concentration and exposure time of certain disinfectants is needed, especially citric acid and peracetic acid.

A Rapid Deep Learning Computer-Aided Diagnosis to Simultaneously Detect and Classify the Novel COVID-19 Pandemic

The novel coronavirus 2019 (COVID-19) becomes recently a global pandemic as declared by the World Health Organization (WHO) in March 2020. COVID-19 rapidly spread and attacked people in more than 200 countries worldwide. The use of artificial intelligence (AI) techniques has become urgent to prevent the exacerbation of the astounding spread of this pernicious disease. This paper presents a novel rapid deep learning computer-aided Diagnosis (CAD) framework for simultaneously detecting and diagnosing the COVID-19 against different respiratory diseases such as pneumonia, atelectasis, cardiomegaly, infiltration, mass, pneumothorax, nodule, and effusion. To develop a useful patient triage system for detecting COVID-19 in early-stage, rapidly extract the visual diagnosis knowledge from the input chest X-ray (CXR) images is extremely required. The proposed CAD framework shows its capability to automatically detect and diagnose COVID-19 with a detection accuracy of 96.31% and a classification accuracy of 97.40%. Meanwhile, the real-time prediction speed of 0.0093 seconds is achieved for a single testing CXR image. To achieve a high accuracy of the knowledge extraction from the entire CXR images with a high prediction speed would represent a key for developing a comprehensive and useful smart patient triage system in hospitals and healthcare systems. © 2021 IEEE.