Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir against SARS-CoV-2 RNA dependent RNA polymerase (RdRp): A molecular docking study.

The novel SARS-coronavirus 2 (SARS-CoV-2) poses a global challenge on healthcare and society. For understanding the susceptibility for SARS-CoV-2 infection, the cell type-specific expression of the host cell surface receptor is necessary. The key protein suggested to be involved in host cell entry is angiotensin I converting enzyme 2 (ACE2). Here, we report the expression pattern of ACE2 across > 150 different cell types corresponding to all major human tissues and organs based on stringent immunohistochemical analysis. The results were compared with several datasets both on the mRNA and protein level. ACE2 expression was mainly observed in enterocytes, renal tubules, gallbladder, cardiomyocytes, male reproductive cells, placental trophoblasts, ductal cells, eye, and vasculature. In the respiratory system, the expression was limited, with no or only low expression in a subset of cells in a few individuals, observed by one antibody only. Our data constitute an important resource for further studies on SARS-CoV-2 host cell entry, in order to understand the biology of the disease and to aid in the development of effective treatments to the viral infection.

https://www.embopress.org/doi/pdf/10.15252/msb.20209610

The protein expression profile of ACE2 in human tissues.

Anti-HCV, nucleotide inhibitors, repurposing against COVID-19

The novel SARS-coronavirus 2 (SARS-CoV-2) poses a global challenge on healthcare and society. For understanding the susceptibility for SARS-CoV-2 infection, the cell type-specific expression of the host cell surface receptor is necessary. The key protein suggested to be involved in host cell entry is Angiotensin I converting enzyme 2 (ACE2). Here, we report the expression pattern of ACE2 across >150 different cell types corresponding to all major human tissues and organs based on stringent immunohistochemical analysis. The results were compared with several datasets both on the mRNA and protein level. ACE2 expression was mainly observed in enterocytes, renal tubules, gallbladder, cardiomyocytes, male reproductive cells, placental trophoblasts, ductal cells, eye and vasculature. In the respiratory system, the expression was limited, with no or only low expression in a subset of cells in a few individuals, observed by one antibody only. Our data constitutes an important resource for further studies on SARS-CoV-2 host cell entry, in order to understand the biology of the disease and to aid in the development of effective treatments to the viral infection.

https://biorxiv.org/content/10.1101/2020.03.31.016048v1.full.pdf

The protein expression profile of ACE2 in human tissues

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel coronavirus that has caused a worldwide pandemic of the human respiratory illness COVID-19, resulting in a severe threat to public health and safety. Analysis of the genetic tree suggests that SARS-CoV-2 belongs to the same Betacoronavirus group as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Although the route for viral transmission remains a mystery, SARS-CoV-2 may have originated in an animal reservoir, likely that of bat. The clinical features of COVID-19, such as fever, cough, shortness of breath, and fatigue, are similar to those of many acute respiratory infections. There is currently no specific treatment for COVID-19, but antiviral therapy combined with supportive care is the main strategy. Here, we summarize recent progress in understanding the epidemiological, virological, and clinical characteristics of COVID-19 and discuss potential targets with existing drugs for the treatment of this emerging zoonotic disease.

/pdf/the-hallmarks-of-covid-19-disease-3nnaasvahs.pdf

The hallmarks of COVID-19 disease.

https://www.researchsquare.com/article/rs-15157/v1.pdf?c=1631845457000

COVID-19 spike-host cell receptor GRP78 binding site prediction

Risk assessment of desert pollution on composite high voltage insulators.

Detecting partial discharge signals (PD) emanated due to defects and faults in cable insulation is crucial to cable condition diagnosis However, PD detection proves challenging as the detected signal comes with superimposed noises with various spectrums One of these common noises that are often present on site is stochastic pulse interference (SPI) This type of noise is random in nature, yet it displays a similar shape to the partial discharge generated pulse and, hence, it becomes difficult to remove (de-noise), while maintaining the original signal intact In this paper SPI noise is mathematically modeled and is combined with the PD signal for signal processing The maximal overlap wavelet transform (MODWT) is then used to de-noise the signal using various parameters Over a wide range of noise magnitude, the parameters that offer best de-noising are subsequently identified Comparing the MODWT technique with the empirical Bayesian WT (EBWT) proves it to be superior

Detection and de-noising of PD signal contaminated with stochastic pulse interference using maximal overlap discrete wavelet transform

Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

Mohammed El-Shahat

Papers

COVID-19 spike-host cell receptor GRP78 binding site prediction

Risk assessment of desert pollution on composite high voltage insulators.

Detection and de-noising of PD signal contaminated with stochastic pulse interference using maximal overlap discrete wavelet transform

Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform