SARS-CoV-2 has spread very quickly from its first reported case on 19 January 2020 in the United Stated of America, leading WHO to declare pandemic by 11 March 2020. RNA viruses accumulate mutations following replication and passage in human population, which prompted us to determine the rate and the regions (hotspots) of the viral genome with high rates of mutation. We analyzed the rate of mutation accumulation over a period of 11 weeks (submitted between 19th January to 15 April 2020) in USA SARS-CoV-2 genome. Our analysis identified that majority of the viral genes accumulated mutations, although with varying rates and these included NSP2, NSP3, RdRp, helicase, Spike, ORF3a, ORF8, and Nucleocapsid protein. Sixteen mutations accumulated in Spike protein in which four mutations are located in the receptor binding domain. Intriguingly, we identified a fair number of viral proteins (NSP7, NSP9, NSP10, NSP11, Envelop, ORF6, and ORF7b proteins), which did not accumulate any mutation. Limited changes in these proteins may suggest that they have conserved functions, which are essential for virus propagation. This provides a basis for a better understanding of the genetic variation in SARS-CoV-2 circulating in the US, which could help in identifying potential therapeutic targets for controlling COVID-19.

Altered expression of many genes and proteins is essential for cancer development and progression. Recently, the affected expression of metadherin (MTDH), also known as AEG-1 (Astrocyte Elevated Gene 1) and Lyric, has been implicated in various aspects of cancer progression and metastasis. Elevated expression of MTDH/AEG-1 has been reported in many cancers including breast, prostate, liver, and esophageal cancers, whereas its expression is low or absent in non-malignant tissues. These expression studies suggest that MTDH may represent a potential tumor associated antigen. MTDH also regulates multiple signaling pathways including PI3K/Akt, NF-κB, Wnt/β-catenin, and MAPK which cooperate to promote the tumorigenic and metastatic potential of transformed cells. Several microRNA have also been found to be associated with the increased MTDH expression in different cancers. Increased MTDH levels were linked to the tumor chemoresistance making it an attractive novel therapeutic target. In this review, we summarize data on MTDH function in various cancers.

Multiple sclerosis (MS) is a neurodegenerative disease characterized by lesions in the central nervous system (CNS). Inflammation and demyelination are the leading causes of neuronal death and brain lesions formation. The immune reactivity is believed to be essential in the neuronal damage in MS. Cytokines play important role in differentiation of Th cells and recruitment of auto-reactive B and T lymphocytes that leads to neuron demyelination and death. Several cytokines have been found to be linked with MS pathogenesis. In the present study, serum level of eight cytokines (IL-1β, IL-2, IL-4, IL-8, IL-10, IL-13, IFN-γ, and TNF-α) was analyzed in USA and Russian MS to identify predictors for the disease. Further, the model was extended to classify MS into remitting and non-remitting by including age, gender, disease duration, Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Severity Score (MSSS) into the cytokines datasets in Russian cohorts. The individual serum cytokines data for the USA cohort was generated by Z score percentile method using R studio, while serum cytokines of the Russian cohort were analyzed using multiplex immunoassay. Datasets were divided into training (70%) and testing (30%). These datasets were used as an input into four machine learning models (support vector machine, decision tree, random forest, and neural networks) available in R programming language. Random forest model was identified as the best model for diagnosis of MS as it performed remarkable on all the considered criteria i.e., Gini, accuracy, specificity, AUC, and sensitivity. RF model also performed best in predicting remitting and non-remitting MS. The present study suggests that the concentration of serum cytokines could be used as prognostic markers for the prediction of MS.

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Considering the prevailing scenario of COVID-19 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare system. Currently, the most common and reliable testing method available for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (rRT-PCR) test. However, the chest radiological (X-ray) imaging can be used as an alternate method to rRT-PCR test, and early COVID-19 symptoms can be investigated by critical examination of patient's chest scans. In the present work, a novel machine learning (ML)-based analytical framework is developed for automatic detection of COVID-19 using chest X-ray (CXR) images of plausible patients. The framework is designed, trained, and validated to identify four classes of CXR images namely, healthy, bacterial pneumonia, viral pneumonia, and COVID-19. The experimental results pose the proposed framework as a potential candidate for COVID-19 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four-class classification. The comparative analysis demonstrates the better capabilities of the proposed framework COVID-19 detection along with other types of pneumonia. K E Y W O R D Schest X-ray images, coronavirus, machine learning methods, pneumonia | INTRODUCTIONIn December 2019, local outbreak of a strange kind of pneumonia due to an unknown source was reported in the city of Wuhan, China. 1 The source of the disease was soon discovered to be a new strain of "Coronavirus" (CoV) termed as "Severe Acute Respiratory Syndrome Coronavirus 2" (SARS-CoV-2) by the international committee on taxonomy of viruses. The disease caused by the virus was named Coronavirus Disease-2019 (COVID-19) by World Health Organisation (WHO) in February 2020. COVID-19 is a highly contagious, upper respiratory syndrome with more than 7 million confirmed infections in about 191 countries as of June 16, 2020. 2 Presently, the disease has been held responsible for causing over a million deaths all across the globe, with the highest trolls in the countries like United States,