Principal Component Analysis Applications in COVID-19 Genome Sequence Studies

Back to Basics: The Diagnostic Value of a Complete Blood Count in the Clinical Management of COVID-19

Since the beginning of the COVID-19 pandemic, scientists have struggled significantly to understand the complexity of COVID-19 pathophysiology. COVID-19 has demonstrated a notoriously unpredictable clinical course. This unpredictability constituted a significant obstacle to clinicians in predicting the disease course among COVID-19 patients, more specifically, in predicting who would develop severe cases and possibly die from the infection. This brief report aims to assess the diagnostic value of using a complete blood count (CBC) and applying high-dimensional analysis, i.e., principal component analysis (PCA), on it to differentiate between patients with mild and severe COVID-19 infection. The data of 855 patients were retrieved from multiple centres in Saudi Arabia. Descriptive statistics, such as counts, percentages, and medians (interquartile ranges) were used to describe patients' characteristics and CBC parameters. Analytical statistics, such as the Mann-Whitney U test, were used to compare between survivors and non-survivors. PCA was applied using the CBC parameters, and the results were compared between survivors and non-survivors. Patients in this study had a median age of 41, with an almost equal ratio of men to women. Most participants were Saudis, and non-survivors were 13.22% of the total cohort. The median values of all CBC indices were within reference ranges; however, some statistically significant differences were observed between survivors and non-survivors. Non-survivors had lower hemoglobin levels and lower hematocrit, lymphocyte, and eosinophil counts but higher WBC and neutrophil counts compared to survivors. PCA on the CBC results of survivors yielded a significantly different profile than non-survivors, indicating the possibility of its use in the context of COVID-19. The diagnostic value of CBC in the clinical management of COVID-19 should be utilized in clinical guidelines for managing COVID-19 cases.

Case-control study of serum vitamin D concentrations in hospitalised patients with COVID-19 and hospitalised controls suffering with respiratory tract infections of differing aetiology

This study explored the prevalence of low serum vitamin D in patients admitted with acute respiratory tract infections (ARTIs) such as COVID-19. This study investigated whether patients with COVID-19 had lower serum vitamin D compared with patients with ARTIs of other aetiology. A case-control study was performed with cases of COVID-19 and controls of non-COVID-19 ARTIs. Patients were enrolled from a single general medical ward in a secondary care hospital between 15 April 2020 and 15 May 2020. Exclusion criteria were an oxygen requirement of >8 L/min. Data collected included serum 25-hydroxyvitamin D concentration, venous plasma glucose concentration and heamoglobin A1c. Outcomes measured were length of hospital stay, deaths, the need for high dependency and intensive care unit involvement. A total of 60 patients of five ethnic groups were enrolled, 85% (n=46) were of White-British ethnicity. The data analysis is based on these 46 patients of which 24 were non-COVID-19 patients with ARTI and 22 were patients with COVID-19. Overall, 80% of the study population had a serum vitamin D concentration below 50 nmol/L with median concentrations of 30 nmol/L and 35 nmol/L for patients with COVID-19 and non-COVID-19 ARTIs respectively. A Mann-Whitney sign-ranked test with respect to serum vitamin D concentration found no statistically significant difference between cases and controls, p=0.09. There was no significant difference in the length of stay, body mass index and rates of various comorbidities such as diabetes mellitus (DM), hypertension and lung disease in both study groups. However, DM was found to be associated with lower serum vitamin D concentrations. The results of this study support published literature showing an association between low serum vitamin D and ARTIs including COVID-19. However, this study did not identify patients with COVID-19 to have a statistically significant lower serum vitamin D concentration than non-COVID-19 patients with ARTI.

Drivers of Spatial Heterogeneity in the Russian Forest Sector: A Multiple Factor Analysis

This study explored the regional differences in the forest industry and management via a Multiple Factor Analysis approach. The dataset used comprises all Russian regions and 34 variables that comprehensively describe the situation in the sector. Based on the Multiple Factor Analysis, the variables contributing most to the spatial heterogeneity in Russian forestry were divided into industrial and forestry factors. The regions leading in the development of the timber industry are mainly located in the Northwestern and Southeastern parts of Russia. They show similarities in high logging volumes, investment attractiveness, and competitiveness in foreign markets. However, a divergence was found between the Northwestern regions and the Siberian and the Far East territories in terms of forest management factors. The Western part of Russia benefits from the density of the population and infrastructure, and proximity to the national financial centers and European markets. By comparison, Asian Russia suffers from labor shortages caused by negative demographic trends and the negative consequences of climate change, resulting in an increase in forest losses and a lack of control, finance, and transport accessibility due to the vast territory. To alleviate regional inequalities, we propose the introduction of private ownership of forests, support for investment projects, and human capital development.

Principal Component Analysis as a Statistical Tool for Concrete Mix Design

With the recent and rapid development of concrete technologies and the ever-increasing use of concrete, adapting concrete to the specific needs and applications of civil engineering is necessary. Due to economic considerations and care for the natural environment, improving the methods currently used in concrete design is also necessary. In this study, the author used principal component analysis as a statistical tool in the concrete mix design process. Using a combination of PCA variables and 2D and 3D factors has made it possible to refine concrete recipes. Thirty-eight concrete mixes of different aggregate grades were analyzed using this method. The applied statistical analysis showed many interesting relationships between the properties of concrete and the content of its components such as the clustering of certain properties, showing dependence between the properties and the quantities of certain ingredients in concrete, and reducing noise in the data, which most importantly simplifies interpretation. This method of analysis can be used as an aid for concrete mix design.