ABSTRACT
Since its first report in December 2019, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly emerged as a pandemic affecting nearly all countries worldwide. As the COVID-19 pandemic progresses, the need to identify genetic risk factors for susceptibility to this serious illness has emerged. Host genetic factors, along with other risk factors may help determine susceptibility to respiratory tract infections. It is hypothesized that the ACE2 gene, encoding angiotensin-converting enzyme 2 (ACE2), is a genetic risk factor for SARS-CoV-2 infection and is required by the virus to enter cells. Together with ACE2, transmembrane protease serine 2 (TMPRSS2) and dipeptidyl peptidase-4 (DPP4) also play an important role in disease severity. Evaluating the role of genetic variants in determining the direction of respiratory infections will help identify potential drug target candidates for further study in COVID-19 patients. We have summarized the latest reports demonstrating that ACE2 variants, their expression, and epigenetic factors may influence an individual’s susceptibility to SARSCoV-2 infection and disease outcome.
ABSTRACT
As the Coronavirus Disease 2019 (COVID-19) pandemic continues to grow globally, testing to detect COVID-19 and isolating individuals who test positive remains to be the primary strategy for preventing community spread of the disease. The current gold standard method of testing for COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) test. The RT-PCR test, however, has an imperfect sensitivity (around 70%), is time-consuming and labor-intensive, and is in short supply, particularly in resource-limited countries. Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities such as chest X-ray and Computed Tomography, which are more widely available and accessible, can be beneficial as an alternative diagnostic tool. We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). We refer to this model as Artificial Intelligence for Detection of COVID-19 (AIDCOV). The hierarchical structure in AIDCOV captures the dependency of features and improves model performance while the attention mechanism makes the model interpretable and transparent. Using a publicly available dataset of 5801 chest images, we demonstrate that our model achieves a mean cross-validation accuracy of 97.8%. AIDCOV has a sensitivity of 99.3%, a specificity of 99.98%, and a positive predictive value of 99.6% in detecting COVID-19 from chest radiography images. AIDCOV can be used in conjunction with or instead of RT-PCR testing (where RT-PCR testing is unavailable) to detect and isolate individuals with COVID-19 and prevent onward transmission to the general population and healthcare workers.
ABSTRACT
Altered cellular metabolism is a hallmark of tumorigenesis. Described first in 1924 by Otto Warburg, a cancer cell undergoes complete metabolic reprogramming to attain nutrient self-sufficiency for proliferation and survival. Interplay between diverse signalling cascades confers this metabolic advantage. In this review we focus on signalling molecules that regulate this altered metabolic paradigm in a cancer cell with emphasis on small molecule mediated intervention for attenuation of growth and progression of tumor.
