Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

Did COVID-19 enlarge spatial disparities in population dynamics? A comparative, multivariate approach for Italy.

A short-term issue that has been occasionally investigated in the current literature is if (and, eventually, how) population dynamics (directly or indirectly) driven by COVID-19 pandemic have contributed to enlarge regional divides in specific demographic processes and dimensions. To verify this assumption, our study run an exploratory multivariate analysis of ten indicators representative of different demographic phenomena (fertility, mortality, nuptiality, internal and international migration) and the related population outcomes (natural balance, migration balance, total growth). We developed a descriptive analysis of the statistical distribution of the ten demographic indicators using eight metrics that assess formation (and consolidation) of spatial divides, controlling for shifts over time in both central tendency, dispersion, and distributional shape regimes. All indicators were made available over 20 years (2002-2021) at a relatively detailed spatial scale (107 NUTS-3 provinces) in Italy. COVID-19 pandemic exerted an impact on Italian population because of intrinsic (e.g. a particularly older population age structure compared with other advanced economies) and extrinsic (e.g. the early start of the pandemic spread compared with the neighboring European countries) factors. For such reasons, Italy may represent a sort of 'worst' demographic scenario for other countries affected by COVID-19 and the results of this empirical study can be informative when delineating policy measures (with both economic and social impact) able to mitigate the effect of pandemics on demographic balance and improve the adaptation capacity of local societies to future pandemic's crises.

Economic vulnerability assessment of EU countries to the impact of COVID-19 pandemic with the revised CEV index.

The COVID-19 pandemic had a wide range of detrimental consequences for the global and national economies. It is vital to identify particularly susceptible areas to adopt effective strategies to alleviate the adverse effects of a pandemic. The objective of the paper is to assess the economic vulnerability of EU countries to the COVID-19 pandemic impact using the revised CEV Index. In the study, methods of multivariate statistics were used to analyse the effects of the pandemic. The revised CEVI replaces the 20-dimensional set of features with one aggregate measure, estimated for 27 EU Member States. According to the study, the economic vulnerability of EU countries to the COVID-19 pandemic varies significantly. The most vulnerable countries are in southern Europe, where the tourism sector plays a significant role in GDP composition. Highly susceptible are also Baltic countries: Latvia and Lithuania. The pandemic's harmful impact was the least seen in Germany and Scandinavian countries. The results of this study can be used as a tool for the formulation of policies aimed at overcoming the adverse consequences of economic vulnerability. The CEVI indicates certain areas in the country's economy that make it more fragile. Thus, it can play a significant role in the decision-making process. In the event of a pandemic shock, the CEVI, in combination with other tools, can be an effective instrument for improving the economy's resilience and helping it recover faster.

Evolution of social mood in Spain throughout the COVID-19 vaccination process: a machine learning approach to tweets analysis.

OBJECTIVES: This paper presents a new approach based on the combination of machine learning techniques, in particular, sentiment analysis using lexicons, and multivariate statistical methods to assess the evolution of social mood through the COVID-19 vaccination process in Spain. METHODS: Analysing 41,669 Spanish tweets posted between 27 February 2020 and 31 December 2021, different sentiments were assessed using a list of Spanish words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and three valences (neutral, negative and positive). How the different subjective emotions were distributed across the tweets was determined using several descriptive statistics; a trajectory plot representing the emotional valence vs narrative time was also included. RESULTS: The results achieved are highly illustrative of the social mood of citizens, registering the different emerging opinion clusters, gauging public states of mind via the collective valence, and detecting the prevalence of different emotions in the successive phases of the vaccination process. CONCLUSIONS: The present combination in formal models of objective and subjective information would therefore provide a more accurate vision of social reality, in this case regarding the COVID-19 vaccination process in Spain, which will enable a more effective resolution of problems.

A NOTE ON miRNA PAIRS SELECTION USING A MULTIVARIATE STATISTICS APPROACH

With the U.S. Food and Drug Administration's (FDA) approval and widespread use of COVID mRNA vaccines (principally the Pfizer-BioNTech and Moderna COVID vaccines), mRNA techniques have become widely recognized by both the media and the general public. Correspondingly, many topics related to these techniques have attracted significant interest in many research areas, including microRNA (miRNA), which regulates many mRNA types. Although miRNA has been researched since early 2000, the studies focused on miRNA in the context of individual diseases are all very recent. What constitutes an appropriate miRNA pair for a biomarker to support disease diagnosis is still an open question in many biochemical and medical investigations, for example, Alzheimer's disease. Sometimes, synthetic (artificial) miRNA is used as a normalizer (denominator of biomarker). Sometimes, a ubiquitous normalizer with a robust concentration value across many pathologies is chosen. In the biomedical field, researchers have selected markers in different ways, often without rigorous mathematical or statistical study. In this paper, instead of using these pathology-insensitive miRNAs as normalizers, we propose a new miRNA-pairs-selection algorithm with a multivariate statistics approach to search for a pair or of pathology-sensitive miRNAs for a given pathology. We demonstrate the performance of this algorithm through a published experiment using published Mild Cognitive Impairment (MCI) data.

Plasma Metabolome Alterations Discriminate between COVID-19 and Non-COVID-19 Pneumonia.

Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 ± 10 %. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and non-COVID-19 pneumonia.

Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis.

One of the difficulties in monitoring an ongoing pandemic is deciding on the metric that best describes its status when multiple intercorrelated measurements are available. Having a single measure, such as the effective reproduction number [Formula: see text], has been a simple and useful metric for tracking the epidemic and for imposing policy interventions to curb the increase when [Formula: see text]. While [Formula: see text] is easy to interpret in a fully susceptible population, it is more difficult to interpret for a population with heterogeneous prior immunity, e.g. from vaccination and prior infection. We propose an additional metric for tracking the UK epidemic that can capture the different spatial scales. These are the principal scores from a weighted principal component analysis. In this paper, we have used the methodology across the four UK nations and across the first two epidemic waves (January 2020-March 2021) to show that first principal score across nations and epidemic waves is a representative indicator of the state of the pandemic and is correlated with the trend in R. Hospitalizations are shown to be consistently representative; however, the precise dominant indicator, i.e. the principal loading(s) of the analysis, can vary geographically and across epidemic waves. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Self-Perceived Emotional Intelligence Levels in Nursing Students in Times of a Pandemic: Multivariate Representation.

Self-perceived emotional intelligence in healthcare personnel is not just an individual skill but a work tool, which is even more necessary in times of crisis. This article aimed to determine emotional intelligence as perceived by students studying nursing at the University of Colima, Mexico, a year after the start of the COVID-19 pandemic. A cross-sectional survey of an academic year stratified population of 349 students was conducted, using the Trait Meta-Mood Scale-24 instrument. A global descriptive analysis was performed for each school year. Additionally, an ANOVA was performed, and a Multiple Correspondence Analysis was executed. It is essential to highlight the high percentages for emotional attention within the results. However, a large percentage of students required improvement in emotional attention, clarity, and repair. According to their school year, significant differences were observed among student groups within the three emotional intelligence subscales (p < 0.05). Second-year students had low levels in the three subscales of emotional intelligence, while fourth-year students had adequate levels. We established that the scores were different depending on the school year, with a significant decrease in second-year students. The implementation of educational programs could aid in the development of emotional skills in students from the health field, especially in times of crisis.

Prediction of two novel overlapping ORFs in the genome of SARS-CoV-2.

Six candidate overlapping genes have been detected in SARS-CoV-2, yet current methods struggle to detect overlapping genes that recently originated. However, such genes might encode proteins beneficial to the virus, and provide a model system to understand gene birth. To complement existing detection methods, I first demonstrated that selection pressure to avoid stop codons in alternative reading frames is a driving force in the origin and retention of overlapping genes. I then built a detection method, CodScr, based on this selection pressure. Finally, I combined CodScr with methods that detect other properties of overlapping genes, such as a biased nucleotide and amino acid composition. I detected two novel ORFs (ORF-Sh and ORF-Mh), overlapping the spike and membrane genes respectively, which are under selection pressure and may be beneficial to SARS-CoV-2. ORF-Sh and ORF-Mh are present, as ORF uninterrupted by stop codons, in 100% and 95% of the SARS-CoV-2 genomes, respectively.

Endemic-epidemic models to understand COVID-19 spatio-temporal evolution.

We propose an endemic-epidemic model: a negative binomial space-time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterized by similar non-pharmaceutical policy interventions.

New insights into the evolutionary features of viral overlapping genes by discriminant analysis.

Overlapping genes originate by a mechanism of overprinting, in which nucleotide substitutions in a pre-existing frame induce the expression of a de novo protein from an alternative frame. In this study, I assembled a dataset of 319 viral overlapping genes, which included 82 overlaps whose expression is experimentally known and the respective 237 homologs. Principal component analysis revealed that overlapping genes have a common pattern of nucleotide and amino acid composition. Discriminant analysis separated overlapping from non-overlapping genes with an accuracy of 97%. When applied to overlapping genes with known genealogy, it separated ancestral from de novo frames with an accuracy close to 100%. This high discriminant power was crucial to computationally design variants of de novo viral proteins known to possess selective anticancer toxicity (apoptin) or protection against neurodegeneration (X protein), as well as to detect two new potential overlapping genes in the genome of the new coronavirus SARS-CoV-2.