Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection.

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Investigation of target sequencing of SARS-CoV-2 and immunogenic GWAS profiling in host cells of COVID-19 in Vietnam.

BACKGROUND: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. METHOD: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. RESULT: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models' predictive capabilities. CONCLUSION: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.

Evaluation in a Cytokine Storm Model In Vivo of the Safety and Efficacy of Intravenous Administration of PRS CK STORM (Standardized Conditioned Medium Obtained by Coculture of Monocytes and Mesenchymal Stromal Cells).

Our research group has been developing a series of biological drugs produced by coculture techniques with M2-polarized macrophages with different primary tissue cells and/or mesenchymal stromal cells (MSC), generally from fat, to produce anti-inflammatory and anti-fibrotic effects, avoiding the overexpression of pro-inflammatory cytokines by the innate immune system at a given time. One of these products is the drug PRS CK STORM, a medium conditioned by allogenic M2-polarized macrophages, from coculture, with those macrophages M2 with MSC from fat, whose composition, in vitro safety, and efficacy we studied. In the present work, we publish the results obtained in terms of safety (pharmacodynamics and pharmacokinetics) and efficacy of the intravenous application of this biological drug in a murine model of cytokine storm associated with severe infectious processes, including those associated with COVID-19. The results demonstrate the safety and high efficacy of PRS CK STORM as an intravenous drug to prevent and treat the cytokine storm associated with infectious processes, including COVID-19.

Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019-2021).

Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019-2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO2, O3, PM10, PM2.5, and SO2, and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O3). Pollution change in susceptibility is more likely to occur in NO2 decrease and O3 increase, but unsusceptibility is more likely to occur in CO and SO2, to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM10 and PM2.5. The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns.

Portuguese Version of COVID-19 Perceived Risk Scale and COVID-19 Phobia Scale: Psychometric Properties.

The COVID-19 pandemic scenario has a psychological impact on individuals and society. A higher level of perceived risk concerning COVID-19 has been found when compared to other potential health threats. A misperception of risk in contrast with the real risk may lead people to develop disruptive cognitive, affective, or behavioral responses to the COVID-19 pandemic, namely, coronaphobia. Validated instruments are needed to evaluate such responses. This work aims to validate the COVID-19 Perceived Risk Scale (C19PRS) and the COVID-19 Phobia Scale (C19PS) in the Portuguese population. The two scales were translated from English to Portuguese using the back-translation technique. The cultural adaptation was framed in the context of establishing the validity and reliability of the instruments. In two studies, C19PRS and C19PS were validated for the adult Portuguese population (N = 1122; women = 725 (64.6%); mean age of 31.91 years old) through exploratory factorial analysis, followed by a confirmatory factorial analysis. Convergent validity was calculated by composite reliability (CR) and average variance extracted (AVE) values. Discriminant validity was assessed by square roots of the AVE values and their comparison with the C19PRS and C19PS dimensions' cross-correlations. Both C19PRS and C19PS present a good adjustment model and solid reliability and validity and have significant correlations with fear of COVID-19 and COVID-19 anxiety scales.

Influence of Forest Visitors' Perceived Restorativeness on Social-Psychological Stress.

This study was conducted to verify the perceived restorativeness of citizens visiting forests on social-psychological stress and psychological resilience according to forest space type. The study involved a questionnaire survey conducted on citizens who visited forests between 1 May and 15 July 2020, when social distancing in daily life was being implemented. Three types of forest spaces (urban forest, national park, and natural recreation forest) were selected for the survey. They used the survey results of 1196 people as analysis data for this study. In this study, the PRS (Perceived Restorativeness Scale) and the PWI-SF (Psychosocial Well-being Index Short Form) were used to evaluate perceived restorativeness and social-psychological stress of citizens visiting forests. In the study, the average score of visitors' perceived restorativeness was 5.31 ± 0.77. Social-psychological stress was found in the healthy group, potential stress group, and high-risk group. These groups made up 8.0%, 82.5%, and 9.5% of the respondents, respectively. Pearson's correlation analysis between perceived restorativeness and social-psychological stress revealed that the higher the perceived restorativeness, the lower the social-psychological stress. "Diversion Mood", "Not bored", and "Coherence", which are the sub-factors of perceived restorativeness according to the forest space type, were found to have meaningful results for psychological resilience. However, there was no significant difference in the forest space type between "Compatibility" and social-psychological stress, which are sub-factors of perceived restorativeness. In conclusion, the forest space type affects the psychological resilience of those who visit the forest. Urban forests, national parks, and natural recreation forests are places to reduce stress.