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This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.
This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.The COVID-19 pandemic has revealed the susceptibility of certain populations to RNA virus infection. This variety of agents is currently the cause of severe respiratory diseases (SARS-CoV2 and Influenza), Hepatitis C, measles and of high prevalence tropical diseases that are detected throughout the year (Dengue and Zika). The rs10774671 polymorphism is a base change from G to A in the last nucleotide of intron-5 of the OAS1 gene. This change modifies a splicing site and generates isoforms of the OAS1 protein with a higher molecular weight and a demonstrated lower enzymatic activity. The low activity of these OAS1 isoforms makes the innate immune response against RNA virus infections less efficient, representing a previously unattended risk factor for certain populations.
Determine the distribution of rs10774671 in the open population of Mexico.
In 98 healthy volunteers, allelic and genotypic frequencies were determined by qPCR using allele specific labeled probes, and the Hardy-Weinberg equilibrium was determined.
The A-allele turned out to be the most prevalent in the analyzed population.
Our population is genetically susceptible to RNA virus disease due to the predominant presence of the A allele of rs10774671 in the OAS1 gene.
Our population is genetically susceptible to RNA virus disease due to the predominant presence of the A allele of rs10774671 in the OAS1 gene.
Arterial hypertension (AH) is implicated in vascular health and contributes significantly to cardiovascular morbidity and mortality. In addition to the contribution of usual risk factors for AH, elucidating the influence of genetic factors is a promising area of investigation. Therefore, we evaluated the association between AH and cardiovascular risk factors (CVRFs) and genetic polymorphisms in communities in Southeast Brazil.
A total of 515 adults aged 18-91 years, who were cross-sectionally assessed between 2015-2016, were included. Demographic, clinical, behavioral, anthropometric characteristics, and laboratory parameters and 12 single nucleotide polymorphisms in seven candidate genes involved in cardiovascular risk (
,
,
,
,
,
,
,
, and
) were evaluated, with AH as the outcome. Sex, age, and laboratory parameters were considered the main confounding factors.
There was a significant association between age >60 years (odds ratio [OR] =6.74), alcohol dependence (OR=3.84), smoking (OR=1.74), overweight (OR=1.74), high plasma triglyceride (TG) levels (OR=1.98) and low high-density lipoprotein (HDL-c) (OR=6.22), diabetes (OR=3.68), and insulin resistance (OR=2.40) and AH. A significant association was observed between rs4721 in
and AH. The T allele in homozygosis was a potent chance modifier for AH. The highest chance gradients for AH were characterized by the presence of the TT genotype and DMT2 (OR=9.70), high TG (OR=6.26), low HDL-c (OR=8.20), and age more than 60 years (OR=9.96).
The interaction of the T allele of the rs4721 polymorphism in
with CVRFs may predispose carriers to a higher cardiovascular risk.
The interaction of the T allele of the rs4721 polymorphism in RARRES2 with CVRFs may predispose carriers to a higher cardiovascular risk.This study was performed to investigate published literature about the association between measles, mumps, and rubella (MMR) vaccine and COVID-19. This is a systematic review in which the databases of Chocrane, Pubmed, Scopus, Web of Science as well as reliable journals including Lancet, New England Journal of Medicine, Jama and also Centers for Disease Control and Prevention (CDC) publications were searched.Out of 169 documents discovered during the literature review, 56 ones were somehow related to the association between MMR vaccine and COVID-19, of which 11 ones mentioned the association between these two, and 8 of them contained a hypothesis about this relationship. A quasi-trial study reported the positive effect of the MMR vaccine on reducing the severity of COVID-19 symptoms among those who received it. Also, a cross-sectional study showed an association between the level of Immunoglobulin G (IgG) mumps and COVID-19. Moreover, a genomic data analysis study also reported the effect of Rubella Immunoglobulin G (IgG) level on COVID-19. It seems that due to the similarity of respiratory diseases including measles, rubella, and mumps to COVID-19, MMR vaccine should be investigated more deeply to see if it is effective in order to deal with this novel disease.The utility of a multi-hazard risk-scape at the county level is significant for county, state, regional, and national policy makers who rely on broad and consistent assessments of hazard exposure and losses. In previous work, the Patterns of Risk using an Integrated Spatial Multi-Hazard (PRISM) approach creates an index of county risk for this purpose. While helpful across large areas, the approach lacks information needed at more localized scales. In this paper, we employ the PRISM approach to all 2015 census tracts in the US. Use of a land-cover approach, with spatial extents and modeled data from 11 natural and 4 technological hazard types, determines spatial exposures. Furthermore, census counts allow for the estimation of population exposures in each tract by hazard type. The results of the tract-level index reveal exposure patterns that contrast the original PRISM model, with a concentration of risk shifting eastward. Eeyarestatin 1 The distribution of land-cover and population exposure more closely resemble the county index, revealing the importance of scale and land-cover considerations, along with the need for additional investigation of risk drivers. We provide an application of the risk and multi-hazard exposures in two major metropolitan areas to demonstrate utility of the approach at this scale.We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.Zinc (Zn2+) is stored in the nucleus, endoplasmic reticulum (ER), Golgi apparatus, mitochondria, lysosomes, and zinc-binding proteins. The acidity of the microenvironment affects the binding between zinc and proteins in which zinc become free or loosely bound. In this study, when cells were treated with an acidic medium, we started seeing free zinc 'hot spots' or zincosomes where we found bright zinc fluorescence. The rising free zinc quickly across whole cells with both intensity and distribution were pH-dependent. Interestingly, the nucleus was more sensitive to acidic treatment as the increase of nuclear zinc was faster and higher than the increase of cytosolic zinc. In addition, we re-cultured strong acid-challenged cells in a normal medium. Comparing to the control, these cells exhibited multiple zinc 'hot spots' beside the nucleus, suggesting that free zinc became more extensively distributed. To investigate further the function of zinc in cell shaping and morphological changes, we categorized strong acid-challenged cells into different shapes and found that the proportion of each cell shape had changed after the acid challenge. These acid-induced changes of the cell shape percentage were partially reversed by the reduction of zinc, suggesting that zinc participated in directing the cell shapes and morphologies during cell growth. Our findings reveal that acidic pH affects the dynamics of cellular zinc by making zinc more accessible to cellular compartments and zinc-binding proteins, which provided new insights into understanding the cellular behavior and the function of zinc in it.Coronavirus is a respiratory disease that spreads globally. The severity and mortality risk of the disease is significant in the elderly, peoples having co-morbidities, and immunosuppressive patients. The outbreak of the pandemic created significant barriers to diagnosis, treatment and follow-up of chronic diseases. Delivering regular and routine comprehensive care for chronic patients was disrupted due to closures of healthcare facilities, lack of public transportation or reductions in services. The purpose of this narrative review was to update how patients with chronic care were affected during the pandemic, healthcare utilization services and available opportunities for better chronic disease management during the pandemic in resources limited settings. Moreover, this review may call to the attention of concerned bodies to make decisions and take measures in the spirit of improving the burden of chronic diseases by forwarding necessary recommendations for possible change and to scale up current intervention programs.
Identifying the disease-associated interactions between different genes helps us to find novel therapeutic targets and predictive biomarkers.
Gene expression data GSE82050 from H1N1 and control human samples were acquired from the NCBI GEO database. Highly co-expressed genes were grouped into modules. Through Person's correlation coefficient calculation between the module and clinical phenotype, notable modules were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted, and the hub genes within the module of interest were identified. Also, gene expression data GSE27131 were acquired from the GEO database to verify differential key gene expression analysis. The CIBERSORT was used to evaluate the immune cells infiltration and the GSVA was performed to identify the differentially regulated pathways in H1N1. The receiver operating characteristic (ROC) curves were used to assess the diagnostic values of the hub genes.
The black module was shown to have the highest correlation with the clinical phenotype, mainly functioning in the signaling pathways such as the mitochondrial inner membrane, DNA conformation change, DNA repair, and cell cycle phase transition.
