RESUMEN
Background: Coronavirus disease (COVID-19) has now morphed into the most serious healthcare challenge that the world has faced in a century. The coronavirus disease (COVID-19) was declared as a public health emergency of international concern (PHEIC) on January 30, 2020, and a pandemic on March 11 by the World Health Organization (WHO). The number of cases and the death toll are rapidly increasing frequently because of its fast transmission from human to human through droplets, contaminated hands or body, and inanimate surfaces. Objective(s): SDS has been found to exhibit broad-spectrum and effective microbicidal and viral inactivation agents through the denaturation of both envelope and non-envelop proteins Methods: Viable SARS-COV-2 particles may also be found on contaminated sites such as steel surfaces, plastic surfaces, stainless steel, cardboard, and glass surfaces that can serve as a source of virus transmission. We reviewed the available literature about the SARS-CoV-2 persistence on inanimate surfaces as well as the decontamination strategies of corona and other viruses by using Sodium dodecyl sulfate (SDS) as well as other cleaning chemicals and disinfectants. Result(s): The efficacy of SDS has been amply demonstrated in several studies involving human immunodeficiency virus (HIV), human papillomavirus (HPV) and herpes simplex virus (HSV). SDS has also been found as deactivator of SARS-CoV-2. In toxic profile, up to 1% concentration of SDS is safe for humans and showed no toxic effect if ingested. Conclusion(s): Since no specific treatment is available as yet so containment and prevention continue to be important strategies against COVID-19. In this context, SDS can be an effective chemical disinfectant to slow and stop the further transmissions and spread of COVID-19.Copyright © 2021 Bentham Science Publishers.
RESUMEN
Rapid and precise diagnosis of COVID-19 is one of the major challenges faced by the global community to control the spread of this overgrowing pandemic. In this article, a hybrid neural network is proposed, named CovTANet, to provide an end-to-end clinical diagnostic tool for early diagnosis, lesion segmentation, and severity prediction of COVID-19 utilizing chest computer tomography (CT) scans. A multiphase optimization strategy is introduced for solving the challenges of complicated diagnosis at a very early stage of infection, where an efficient lesion segmentation network is optimized initially, which is later integrated into a joint optimization framework for the diagnosis and severity prediction tasks providing feature enhancement of the infected regions. Moreover, for overcoming the challenges with diffused, blurred, and varying shaped edges of COVID lesions with novel and diverse characteristics, a novel segmentation network is introduced, namely tri-level attention-based segmentation network. This network has significantly reduced semantic gaps in subsequent encoding-decoding stages, with immense parallelization of multiscale features for faster convergence providing considerable performance improvement over traditional networks. Furthermore, a novel tri-level attention mechanism has been introduced, which is repeatedly utilized over the network, combining channel, spatial, and pixel attention schemes for faster and efficient generalization of contextual information embedded in the feature map through feature recalibration and enhancement operations. Outstanding performances have been achieved in all three tasks through extensive experimentation on a large publicly available dataset containing 1110 chest CT-volumes, which signifies the effectiveness of the proposed scheme at the current stage of the pandemic.