The Molecular Basis of Gender Variations in Mortality Rates Associated With the Novel Coronavirus (COVID-19) Outbreak

COVIDanno, COVID-19 annotation in human

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected in vitro model, allows easy control of the experimental conditions, obtaining reproducible results, and monitoring of infection progression. Currently, accumulating RNA-seq data from SARS-CoV-2 in vitro models urgently needs systematic translation and interpretation. To fill this gap, we built COVIDanno, COVID-19 annotation in humans, available at http://biomedbdc.wchscu.cn/COVIDanno/. The aim of this resource is to provide a reference resource of intensive functional annotations of differentially expressed genes (DEGs) among different time points of COVID-19 infection in human in vitro models. To do this, we performed differential expression analysis for 136 individual datasets across 13 tissue types. In total, we identified 4,935 DEGs. We performed multiple bioinformatics/computational biology studies for these DEGs. Furthermore, we developed a novel tool to help users predict the status of SARS-CoV-2 infection for a given sample. COVIDanno will be a valuable resource for identifying SARS-CoV-2-related genes and understanding their potential functional roles in different time points and multiple tissue types.

Infection, Dysbiosis and Inflammation Interplay in the COVID Era in Children

For over three years, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in children and adolescents has generated repercussions, especially a few weeks after infection, for symptomatic patients who tested positive, for asymptomatic ones, or even just the contacts of an infected person, and evolved from severe forms such as multisystem inflammatory syndrome in children (MIS-C) to multifarious clinical manifestations in long COVID (LC). Referred to under the umbrella term LC, the onset of persistent and highly heterogeneous symptoms such as fatigue, post-exertion malaise, cognitive dysfunction, and others have a major impact on the child's daily quality of life for months. The first aim of this review was to highlight the circumstances of the pathophysiological changes produced by COVID-19 in children and to better understand the hyperinflammation in COVID-19 and how MIS-C, as a life-threatening condition, could have been avoided in some patients. Another goal was to better identify the interplay between infection, dysbiosis, and inflammation at a molecular and cellular level, to better guide scientists, physicians, and pediatricians to advance new lines of medical action to avoid the post-acute sequelae of SARS-CoV-2 infection. The third objective was to identify symptoms and their connection to molecular pathways to recognize LC more easily. The fourth purpose was to connect the triggering factors of LC with related sequelae following acute SARS-CoV-2 injuries to systems and organs, the persistence of the virus, and some of its components in hidden reservoirs, including the gut and the central nervous system. The reactivation of other latent infectious agents in the host's immune environments, the interaction of this virus with the microbiome, immune hyperactivation, and autoimmunity generated by molecular mimicry between viral agents and host proteins, could initiate a targeted and individualized management. New high-tech solutions, molecules, probiotics, and others should be discovered to innovatively solve the interplay between RNA persistent viruses, microbiota, and our immune system.

Peripheral blood cell anomalies in COVID-19 patients in the United Arab Emirates: A single-centered study

Introduction

In this study, we aimed at exploring the morphologic and quantitative abnormalities in the peripheral blood counts of coronavirus disease 2019 (COVID-19) patients.

Methods

A cohort of 131 COVID-19 patients was recruited at University Hospital Sharjah (UHS), UAE. Their peripheral blood smears were examined for morphological evaluation. Also, their clinical laboratory investigations and radiological findings were retrieved from the medical records. Our cohort consisted of 63 males and 68 females with an age of 63.6 ± 18.6 years.

Results

The presence of atypical lymphocytes was observed in around 80% of the recruited COVID-19 patients. Further, monocytes with toxic cytoplasmic vacuoles were identified in 55% of the cases. Neutrophil-associated changes, including pseudo-Pelger-Huët, bands, and long nuclear endoplasm, were reported in around 25-35% of the patients. RBCs associated changes such as microcytic and hypochromic RBCs, as well as targetoid, dacrocytes, ovalocytes, echinocytes/burr cells, and schistocytes, were described. According to disease severity, RBCs chromicity was found to be significantly different between stable and critical patients. COVID-19 patients with CO-RADS 5 showed a similar change in RBCs as well as a decrease in the neutrophils with hypogranular cytoplasm.

Conclusion

Peripheral blood smear assessment in COVID-19 patients could provide information about the disease state and pulmonary involvement.

System analysis of the fast global coronavirus disease 2019 (COVID-19) spread. Can we avoid future pandemics under global climate change?

The recent fast global spread of COVID-19 caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) questions why and how the disease managed to be so effective against existing health protection measures. These measures, developed by many countries over centuries and strengthened over the last decades, proved to be ineffective against COVID-19. The sharp increase in human longevity and current transport systems in economically developing countries with the background of persisting cultural frameworks and stable local pools of high bacterial and viral mutations generated the wide gap between the established health protection systems and the new emerging diseases. SARS-CoV-2 targets human populations over the world with long incubation periods, often without symptoms, and serious outcomes. Hence, novel strategies are necessary to meet the demands of developing economic and social environments. Moreover, the ongoing climate change adds extra challenges while altering the existing system of interactions in biological populations and in human society. Climate change may lead to new sources of viral and microbial mutations, new ways of zoonotic disease transmission and to huge social and economic transformations in many countries. The present short Opinion applies a system approach linking biomedical, climate change, social and economic aspects and, accordingly, discusses the measures and more efficient means to avoid future pandemics.

Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis

MOTIVATION

Advanced deep learning techniques have been widely applied in disease diagnosis and prognosis with clinical omics, especially gene expression data. In the regulation of biological processes and disease progression, genes often work interactively rather than individually. Therefore, investigating gene association information and co-functional gene modules can facilitate disease state prediction.

RESULTS

To explore the gene modules and inter-gene relational information contained in the omics data, we propose a novel multi-level attention graph neural network (MLA-GNN) for disease diagnosis and prognosis. Specifically, we format omics data into co-expression graphs via weighted correlation network analysis, and then construct multi-level graph features, finally fuse them through a well-designed multi-level graph feature fully fusion module to conduct predictions. For model interpretation, a novel full-gradient graph saliency mechanism is developed to identify the disease-relevant genes. MLA-GNN achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomic data from coronavirus disease 2019 (COVID-19)/non-COVID-19 patient sera. More importantly, the relevant genes selected by our model are interpretable and are consistent with the clinical understanding.

AVAILABILITYAND IMPLEMENTATION

The codes are available at https://github.com/TencentAILabHealthcare/MLA-GNN.

The Effects of COVID-19 First Waves in Italy: An Answer Through a Retrospective Analysis of Mortality

Background

Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.

Objective

This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series.

Methods

We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis.

Results

Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t₂₁=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals’ using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006).

Conclusions

Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19–related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.