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The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. Therefore, rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Additionally, general treatments, coronavirus-specific treatments, and antiviral treatments useful in fighting COVID-19 are addressed. This review sets out to shed light on the SARS-CoV-2 and host receptor recognition, a crucial factor for successful virus infection and taking immune-informatics approaches to identify Band T-cell epitopes for surface glycoprotein of SARS-CoV-2. A variety of improved or new approaches also have been developed. It is anticipated that this will assist researchers and clinicians in developing better techniques for timely and effective detection of coronavirus infection. Moreover, the genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three-dimensional structure of the Main protease (Mpro) is available. The reported structure of the target Mpro was described in this review to identify potential drugs for COVID-19 using virtual high throughput screening. 1. Introduction Coronavirus is a type of single-stranded RNA (ssRNA) virus [1] Before the emergence of Sars-CoV-2, there are 6 known human coronaviruses, including the Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV). The symptoms caused by Sars-CoV-2 infection include acute respiratory distress syndrome (~29%), acute cardiac injury (~12%) or acute kidney injury (~7%) [2], implying that Sars-CoV-2 may infect various human tissues. COVID-19 is a highly infectious disease [3,4] associated with high mortality [5]. SARS-CoV-2, the virus responsible for COVID-19, is a betacoronavirus [6]. The previous name for this virus was Sars-CoV-2. The genome of SARS-CoV-2 has been sequenced [7,8]. The genomic sequence of SARS-CoV-2 has 96% similarity to the bat-coronavirus and 76.5% identity to the SARS-CoV [9]. Although there are no approved drugs or vaccines for COVID-19, some clinical

Introduction Coronavirus disease 2019 (COVID-19) emerged in China and spread worldwide. In this study, we assessed the characteristics of markers of the liver in patients with COVID-19 to provide new insights in improving clinical treatment. Patients and Methods We recruited 279 patients who confirmed COVID-19 and the data of liver biomarkers and complete blood count of patients were defined as the day onset when the patients admitted to the hospital. Results The average of LDH value was 621.29 U/L in all patients with COVID-19, and CPK was 286.90 U/L. The average AST was 44.03 U/L in all patients, and ALT was 31.14 U/L. The AST/ALT ratio was 1.64 in all patients. The measurement of CRP was increased by 79.93% in all patients. Average ALT and AST values of patients with elevated ALT were significantly increased in comparison to patients with normal ALT ( P -value = 0.001), while AST/ALT ratio was significantly decreased compared to patients with normal ALT ( P -value= 0.014). In addition, the average LDH of patients with elevated ALT was significantly increased compared to patients with normal ALT ( P -value = 0.014). Conclusion Hepatic injury and abnormal liver enzymes related to COVID-19 infection is an acute non-specific inflammation alteration.

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DNA methylation is one of the epigenetic changes, which plays a major role in regulating gene expression and, thus, many biological processes and diseases. There are several methods for determining the methylation of DNA samples. However, selecting the most appropriate method for answering biological questions appears to be a challenging task. The primary methods in DNA methylation focused on identifying the state of methylation of the examined genes and determining the total amount of 5-methyl cytosine. The study of DNA methylation at a large scale of genomic levels became possible following the use of microarray hybridization technology. The new generation of sequencing platforms now allows the preparation of genomic maps of DNA methylation at the single-open level. This review includes the majority of methods available to date, introducing the most widely used methods, the bisulfite treatment, biological identification, and chemical cutting along with their advantages and disadvantages. The techniques are then scrutinized according to their robustness, high throughput capabilities, and cost.

Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can be used by artificial intelligence and machine learning to predict the confirmed cases of Monkeypox at an early stage. Motivated by this, we propose in this paper a new approach for accurate prediction of the Monkeypox confirmed cases based on an optimized Long Short-Term Memory (LSTM) deep network. To fine-tune the hyper-parameters of the LSTM-based deep network, we employed the Al-Biruni Earth Radius (BER) optimization algorithm; thus, the proposed approach is denoted by BER-LSTM. Experimental results show the effectiveness of the proposed approach when assessed using various evaluation criteria, such as Mean Bias Error, which is recorded as (0.06) using BER-LSTM. To prove the superiority of the proposed approach, six different machine learning models are included in the conducted experiments. In addition, four different optimization algorithms are considered for comparison purposes. The results of this comparison confirmed the superiority of the proposed approach. On the other hand, several statistical tests are applied to analyze the stability and significance of the proposed approach. These tests include one-way Analysis of Variance (ANOVA), Wilcoxon, and regression tests. The results of these tests emphasize the robustness, significance, and efficiency of the proposed approach.

Resveratrol is a polyphenolic antioxidant whose possible health benefits include anticarcinogenic, antiaging, and antimicrobial properties that have gained significant attention. The compound is well accepted by individuals and has been commonly used as a nutraceutical in recent decades. Its widespread usage makes it essential to study as a single agent as well as in combination with traditional prescription antibiotics as regards to antimicrobial properties. Resveratrol demonstrates the action of antimicrobials against a remarkable bacterial diversity, viruses, and fungus. This report explains resveratrol as an all-natural antimicrobial representative. It may modify the bacterial virulence qualities resulting in decreased toxic substance production, biofilm inhibition, motility reduction, and quorum sensing disturbance. Moreover, in conjunction with standard antibiotics, resveratrol improves aminoglycoside efficacy versus Staphylococcus aureus, while it antagonizes the deadly function of fluoroquinolones against S. aureus and also Escherichia coli. The present study aimed to thoroughly review and study the antimicrobial potency of resveratrol, expected to help researchers pave the way for solving antimicrobial resistance.