Gastric pro-inflammatory mediators and cytoprotective factors were determined. An oral administration of a chitosan-curcumin mixture exerted a superior efficacy than curcumin, chitosan or lansoprazole (a standard antiulcer agent) in healing indomethacin-induced GU. It was revealed that the mixture exhibited the highest anti-oxidant, anti-inflammatory and gastric mucus producing activities including the high potency in down-regulating pro-inflammatory COX-2 and iNOS expression but up-regulating cytoprotective COX-1, nNOS and eNOS expression. The present findings indicated the benefit of a chitosan-curcumin mixture as a potential alternative agent in treating NSAIDs-induced gastric ulcer.The present findings indicated the benefit of a chitosan-curcumin mixture as a potential alternative agent in treating NSAIDs-induced gastric ulcer.Coronavirus-19 causing a severe acute respiratory disorder in humans and becoming a major health problem. Its expansion takes place very rapidly throughout the world since it has been first identified in Wuhan, China (December 2019). The causative virus is known as severe acute respiratory syndrome coronavirus 2. compound W13 Microtubule Associated inhibitor And the World Health Organization has named this respiratory syndrome as a new epidemic disease called COVID-19. The incidence of COVID-19 continues to increase with three million confirmed infected cases and with 244,000 death cases worldwide. Still, now there is no specific treatment or vaccine available against COVID- 19. The collective information about the different aspects of COVID-19 viral infection has gathered from renowned journals, and electronic databases including Science Direct, Web of Science, Scopus and PubMed from 1990 to 2020 The current manuscript has highlighted transmission and symptoms. Therefore, the current manuscript also included says how the SARS-CoV 2 can facilitate the debut of the virus into targeted host cells. In December 2019, an outbreak of a pneumonia-like illness, Coronavirusdisease-2019 (COVID-19), originating from Wuhan, China was linked to novel coronavirus, now termed SARS-CoV-2. Unfortunately, no effective drugs or vaccines have been reported yet. The main protease (MPRO) remains the most validated pharmacological target for the design and discovery of inhibitors. The purpose of the study was to find a prospective natural scaffold as an inhibitor for MPRO main protease in SARS-CoV-2 and compare it with repurposed antiviral drugs lopinavir and nelflinavir. Natural compound libraries were screened for potential scaffold against MPRO main protease. Molecular dynamics simulation, MM-GBSA and principle component analyses of enzyme-ligand complexes were carried out with the top-ranking hits and compared with the repurposed antiviral drugs lopinavir and nelfinavir. The structure-based virtual screening indicated phenylbenzopyrone of flavonoids as one of the top-ranking scaffolds that have the potential to inhibit the main protease with O-glycosidic form performing better than corresponding aglyconic form. Simulation studies indicated that glycosidic form of flavonoid as more suitable inhibitor with compounds rutin, procyanidin B6, baicalin and galloylquercetin, demonstrating high affinity and stability, and rutin emerging as one of the best candidate compound. Interestingly, rutin was reported to have inhibitory activity against similar protease (3Cprotease of enterovirus A71) as well as implicated in lung fibrosis. The present study displaying flavonoids, possessing a potential scaffold for inhibiting main protease activity for all betacoronavirus is an attempt to provide new and safe drug leads within a reasonably short period.The present study displaying flavonoids, possessing a potential scaffold for inhibiting main protease activity for all betacoronavirus is an attempt to provide new and safe drug leads within a reasonably short period.Drug-drug interactions may occur when to combine two or more drugs and may cause some adverse events such as Cardiotoxicity, Central neurotoxicity, Hepatotoxicity, etc. Although a large number of researchers who are proficient in pharmacokinetics and pharmacodynamics have been engaged in drug assays and trying to find out the side effects of all kinds of drug combinations. However, at the same time, the number of new drugs is increasing dramatically, and the drug assay is an expensive and time-consuming process. It is impossible to find all the adverse reactions through drug experiments. Therefore, new attempts have risen in using computational techniques to deal with this problem. In this review, we conduct a review of the literature on applying the computational method for predicting drug-drug interactions. We first briefly introduce the widely used data sets. After that, we elaborate on the existing state-of-art deep learning models for drug-drug interactions prediction. We also discussed the challenges and opportunities of applying the computational method in drug-drug interactions prediction.Mammalian nervous systems depend crucially on myelin sheaths covering the axons. In the central nervous system, myelin sheaths consist of lipid structures which are generated from the membrane of oligodendrocytes (OL). These sheaths allow fast nerve transmission, protect axons and provide them metabolic support. In response to specific traumas or pathologies, these lipid structures can be destabilized and generate demyelinating lesions. Multiple sclerosis (MS) is an example of a demyelinating disease in which the myelin sheaths surrounding the nerve fibers of the brain and spinal cord are damaged. MS is the leading cause of neurological disability in young adults in many countries, and its incidence has been increasing in recent decades. Related to its etiology, it is known that MS is an autoimmune and inflammatory CNS disease. However, there are no effective treatments for this disease and the immunomodulatory therapies that currently exist have proven limited success since they only delay the progress of theath", "remyelination", "demyelination", "oligodendrocyte" and "lipid synthesis" were used to focus the search. We favoured papers published after January, 2015, but did not exclude earlier seminal papers.