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

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.

Sequences of the genomes of all-important bacterial pathogens of man, plants, and animals have been completed. Still, it is not enough to achieve complete information of all the mechanisms controlling the biological processes of an organism. Along with all advances in different proteomics technologies, proteomics has completed our knowledge of biological processes all around the world. Proteomics is a valuable technique to explain the complement of proteins in any organism. One of the fields that has been notably benefited from other systems approaches is bacterial pathogenesis. An emerging field is to use proteomics to examine the infectious agents in terms of, among many, the response the host and pathogen to the infection process, which leads to a deeper knowledge of the mechanisms of bacterial virulence. This trend also enables us to identify quantitative measurements for proteins extracted from microorganisms. The present review study is an attempt to summarize a variety of different proteomic techniques and advances. The significant applications in bacterial pathogenesis studies are also covered. Moreover, the areas where proteomics may lead the future studies are introduced.

Background: The occurrence of invasive Candida infections has increased during the past two decades as a result of increasing in the number of immunocompromised patients. Objectives: In this study (cross sectional design), during six months, the prevalence patterns of Candida species isolated from sterile body sites of patients admitted to the general hospital of Milad Intensive Care Units (ICUs) in Tehran (Iran), were determined. Methods: Candidal isolates were obtained from 50 patients admitted to Milad ICUs from April to September 2013. Identification of the isolates was performed using morphological and Polymerase Chain Reaction assay. For identification of Candida at the species level, degenerated and specific primers based on the genomic sequences of DNA topoisomerase II of Candida species were used and their specificities tested by PCR-based identifications. Results: A total of 67 Candida isolates were obtained from 50 patients. Out of 67 Candida isolates, 47.8% were C. glabrata, 28.3% were C. albicans, 7.5% were C. tropicalis, 7.5% were C. guilliermondii, 3% were C. krusei and 4.4% were C. dubliniensis. The main patient group affected by candidal infections was aged 50 to 70 years. Overall, 11.7% of patients had cancer while other diseases such as diabetes were less reported. The mean time of stay at the ICU before identification was 25.3 days (ranging from 2 to 120 days). Conclusions: Increase in the prevalence of non-C. albicans species in the recent years has become a problematic event amongst clinicians caring for ICU patients. Candida glabrata is the most common species isolated from ICU patients in comparison with other species in this study. These findings emphasized on the significance of organizing treatable prevention programs.

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.