Coronavirus Disease 2019 Fatality Research Articles

EFCAB4B (CRACR2A/Rab46) Genetic Variants Associated with COVID-19 Fatality

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in more than 692 million cases worldwide and nearly 7 million deaths (August 2023). Severe COVID-19 is characterised in part by vascular thrombosis and a cytokine storm due to increased plasma concentrations of pro-thrombotic proteins such as von Willebrand factor and cytokines secreted from endothelial and T-cells. EFCAB4B is a gene that encodes for two proteins (CRACR2A and Rab46) that play important roles in endothelial and T-cell secretion. In this study, using patient data recorded in the UK Biobank, we demonstrate the importance of variants in the EFCAB4B genetic sequence with COVID-19 fatality. Using logistic regression analysis, we determined that three single-nucleotide polymorphisms (SNPs) in the gene cause missense variations in CRACR2A and Rab46, which are associated with COVID-19 fatality (rs9788233: p = 0.004, odds ratio = 1.511; rs17836273: p = 0.012, odds ratio = 1.433; rs36030417: p = 0.013, odds ratio = 1.393). All three SNPs cause changes in amino acid residues that are highly conserved across species, indicating their importance in protein structure and function. Two SNPs, rs17836273 (A98T) and rs36030417 (H212Q), cause amino acid substitutions in important functional domains: the EF-hand and coiled-coil domain, respectively. Molecular modelling shows minimal impact by the substitution of threonine at position 98 on the structure of the EF-hand. Since Rab46 is a GTPase that regulates both endothelial cell secretion and T-cell signalling, these missense variants may play a role in the molecular mechanisms underlying the thrombotic and inflammatory characteristics observed in patients with severe COVID-19 outcomes.

Determinant Factors of Mortality in Pre-elderly and Elderly Patients With COVID-19 in Jakarta, Indonesia.

This study aimed to identify risk factors associated with coronavirus disease 2019 (COVID-19) mortality in pre-elderly and elderly individuals in Jakarta, Indonesia. We employed a case-control study design, utilizing secondary data from the Epidemiology Surveillance, Immunization Prevention, and Disease Control Sections of the DKI Jakarta Provincial Health Office, collected from December 2020 to January 2021. The study included 188 cases and an equal number of controls. Cases were COVID-19 patients confirmed to have died, as reported by hospitals and communities and subsequently verified by healthcare workers. Control subjects were patients who completed a 14-day isolation period and had been officially declared recovered by healthcare professionals. The dependent variable was the mortality of COVID-19 patients in the January 2021 period. The independent variables consisted of demographic data (age and sex), clinical symptoms (cough, runny nose, anosmia, diarrhea, headaches, abdominal pain, muscle pain, and nausea/vomiting), and comorbidities (hypertension, heart disease, and diabetes). Multivariate analysis was conducted using multiple logistic regression. The multiple logistic regression analysis revealed several factors associated with COVID-19 fatalities in Jakarta: age of 60 years or older (odds ratio [OR], 4.84; 95% CI, 3.00 to 7.80), male (OR, 2.38; 95% CI, 2.41 to 3.68), dyspnea (OR, 3.93; 95% CI, 2.04 to 7.55), anosmia (OR, 0.13; 95% CI, 0.04 to 0.46), and heart disease (OR, 4.38; 95% CI, 1.04 to 18.46). The control and prevention of COVID-19 among elderly individuals require particular vigilance. When a COVID-19 case is detected within this demographic, prompt treatment and medication administration are crucial to mitigate the presenting symptoms.

Changes in the national prevalence of asthma and coronavirus disease 2019 fatality: A population-based cross-sectional study

BackgroundThe coronavirus disease 2019 (COVID-19) mitigation and containment strategies implemented by each country can influence the prevalence of asthma and its fatality. ObjectiveTo analyze the trend of asthma prevalence and COVID-19 fatality in children and adults with asthma. MethodsThe prevalence of asthma and fatalities were compared among the peaks of 5 pandemic waves in Mexico. ResultsAmong patients with COVID-19, the prevalence rates of asthma among children were as follows: wave I, 3.5%; wave II, 2.6%; wave III, 2.2%; wave IV, 2.4%; and wave V, 1.9% (P for trend < .001); the prevalence rates of asthma among adults were as follows: wave I, 2.5%; wave II, 1.8%; wave III, 1.5%; wave IV, 1.7%; and wave V, 1.6% (P for trend < .001). The rates of fatality because of COVID-19 among individuals with asthma were as follows: wave I, 8.9%; wave II, 7.7%; wave III, 5.0%; wave IV, 0.9%; and wave V, 0.2% (P for trend < .001). ConclusionThe prevalence rates of asthma and fatalities from COVID-19 suggest a gradual reduction throughout the pandemic in Mexico.

Linkages between COVID-19, solar UV radiation, and the Montreal Protocol

There are several connections between coronavirus disease 2019 (COVID-19), solar UV radiation, and the Montreal Protocol. Exposure to ambient solar UV radiation inactivates SARS-CoV-2, the virus responsible for COVID-19. An action spectrum describing the wavelength dependence of the inactivation of SARS-CoV-2 by UV and visible radiation has recently been published. In contrast to action spectra that have been assumed in the past for estimating the effect of UV radiation on SARS-CoV-2, the new action spectrum has a large sensitivity in the UV-A (315–400 nm) range. If this “UV-A tail” is correct, solar UV radiation could be much more efficient in inactivating the virus responsible for COVID-19 than previously thought. Furthermore, the sensitivity of inactivation rates to the total column ozone would be reduced because ozone absorbs only a small amount of UV-A radiation. Using solar simulators, the times for inactivating SARS-CoV-2 have been determined by several groups; however, many measurements are affected by poorly defined experimental setups. The most reliable data suggest that 90% of viral particles embedded in saliva are inactivated within ~ 7 min by solar radiation for a solar zenith angle (SZA) of 16.5° and within ~ 13 min for a SZA of 63.4°. Slightly longer inactivation times were found for aerosolised virus particles. These times can become considerably longer during cloudy conditions or if virus particles are shielded from solar radiation. Many publications have provided evidence of an inverse relationship between ambient solar UV radiation and the incidence or severity of COVID-19, but the reasons for these negative correlations have not been unambiguously identified and could also be explained by confounders, such as ambient temperature, humidity, visible radiation, daylength, temporal changes in risk and disease management, and the proximity of people to other people. Meta-analyses of observational studies indicate inverse associations between serum 25-hydroxy vitamin D (25(OH)D) concentration and the risk of SARS-CoV-2 positivity or severity of COVID-19, although the quality of these studies is largely low. Mendelian randomisation studies have not found statistically significant evidence of a causal effect of 25(OH)D concentration on COVID-19 susceptibility or severity, but a potential link between vitamin D status and disease severity cannot be excluded as some randomised trials suggest that vitamin D supplementation is beneficial for people admitted to a hospital. Several studies indicate significant positive associations between air pollution and COVID-19 incidence and fatality rates. Conversely, well-established cohort studies indicate no association between long-term exposure to air pollution and infection with SARS-CoV-2. By limiting increases in UV radiation, the Montreal Protocol has also suppressed the inactivation rates of pathogens exposed to UV radiation. However, there is insufficient evidence to conclude that the expected larger inactivation rates without the Montreal Protocol would have had tangible consequences on the progress of the COVID-19 pandemic.Graphical abstract

Predictors of COVID-19 Related Fatalities during the First Three Months of the Pandemic: An Ecological Study of 114 Countries

Background: Coronavirus disease 2019 (COVID-19) has costed the lives of more than 2 million people during the first wave of the pandemic with the number of fatalities varying among different countries. Thus, we aimed to assess the associations of specific demographic, clinical and political variables with COVID-19 related fatalities in various countries, during the first three months of the pandemic. Methods: We analyzed publicly available data from 192 regions (114 countries) and built a log-linear regression model with random intercepts in order to identify predictors of COVID-19 fatalities during the first three months of the pandemic. We used country-level data including total population, the percentage of people aged 65 and above, number of ICU beds per 100,000 people, geographical latitude and number of days from the first confirmed COVD-19 case to establishment of specific preventive control measures as explanatory variables of our statistical model. Results: In our multivariate statistical model, one unit increases in the total population (in 10,000,000 units), percentage of population aged 65 and above and the number of days from the first confirmed COVID-19 case to the imposition of preventive measures, were related to 3.8% (95% CI: 0.8% to 6.9%), 7.1% (95% CI: 0.9% to 13.6%) and 1.8% (95% CI: 0.3% to 3.6%), respectively, higher number of COVID-19 related deaths. Conclusion: Our findings imply positives associations of total population, percentage of population aged 65 and above and number of days from the first confirmed COVID-19 case to the imposition of preventive measures with COVID-19 fatal cases, during the first three months of the pandemic. Future non-ecological studies are warranted to confirm our results.

Predictors of COVID-19 Related Fatalities during the First Three Months of the Pandemic: An Ecological Study of 114 Countries

Background: Coronavirus disease 2019 (COVID-19) has costed the lives of more than 2 million people during the first wave of the pandemic with the number of fatalities varying among different countries. Thus, we aimed to assess the associations of specific demographic, clinical and political variables with COVID-19 related fatalities in various countries, during the first three months of the pandemic. Methods: We analyzed publicly available data from 192 regions (114 countries) and built a log-linear regression model with random intercepts in order to identify predictors of COVID-19 fatalities during the first three months of the pandemic. We used country-level data including total population, the percentage of people aged 65 and above, number of ICU beds per 100,000 people, geographical latitude and number of days from the first confirmed COVD-19 case to establishment of specific preventive control measures as explanatory variables of our statistical model. Results: In our multivariate statistical model, one unit increases in the total population (in 10,000,000 units), percentage of population aged 65 and above and the number of days from the first confirmed COVID-19 case to the imposition of preventive measures, were related to 3.8% (95% CI: 0.8% to 6.9%), 7.1% (95% CI: 0.9% to 13.6%) and 1.8% (95% CI: 0.3% to 3.6%), respectively, higher number of COVID-19 related deaths. Conclusion: Our findings imply positives associations of total population, percentage of population aged 65 and above and number of days from the first confirmed COVID-19 case to the imposition of preventive measures with COVID-19 fatal cases, during the first three months of the pandemic. Future non-ecological studies are warranted to confirm our results.

Risk Factors Related to COVID-19 Reinfection and Fatality During the Omicron (BA.1/BA.2) Period in Korea.

This study aimed to investigate the deaths due to coronavirus disease 2019 (COVID-19) reinfection and related risk factors. National cohort data were collected for a six-month period when omicron BA.1/BA.2 variant was dominant in South Korea. The long-term care facility residents (adjusted odds ratio, 3.11; 95% confidence interval [CI], 2.98-3.25) had significantly higher risk of reinfection than the general population. The risk of reinfection was significantly lower for persons with 2 or more vaccine doses compared to the unvaccinated. The risk of death was significantly higher in the reinfection group than in the primary infection group for persons in the 60-74 years age group (adjusted relative risk [aRR], 1.62; 95% CI, 1.19-2.20), and immunocompromised group (aRR, 4.56; 95% CI, 2.34-8.90). In these data, vaccination history was significantly related to reduced COVID-19 reinfection and severe progression, and scheduled vaccinations were important even with a history of infection.

Hospital Strain and COVID-19 Fatality - England, April 2020-March 2022.

During the coronavirus disease 2019 (COVID-19) pandemic, tremendous efforts have been made in countries to suppress epidemic peaks and strengthen hospital services to avoid hospital strain and ultimately reduce the risk of death from COVID-19. However, there is limited empirical evidence that hospital strain increases COVID-19 deaths. We found the risk of death from COVID-19 was linearly associated with the number of patients currently in hospitals, a measure of hospital strain, before the Omicron period. This risk could be increased by a maximum of 188.0%. These findings suggest that any (additional) effort to reduce hospital strain would be beneficial during early large COVID-19 outbreaks and possibly also others alike. During an Omicron outbreak, vigilance remains necessary to prevent excess deaths caused by hospital strain as happened in Hong Kong Special Administrative Region, China.

Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors' global and local influence on the model's outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.

Asthma and COVID-19 pandemic: focus on the eosinophil count and ACE2 expression.

Currently, the world is engaged with a coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection. The Center for Disease Control and Prevention (CDC) has proposed moderate to severe asthma as a risk factor for COVID-19 susceptibility and severity. However, current evidences have not identified asthma in the top 10 comorbidities associated with COVID-19 fatalities. It raises the question that why patients with different type of asthma are not more vulnerable to SARS-CoV-2 infection like other respiratory infection. Increased number of eosinophils and elevated angiotensin-converting enzyme 2 (ACE2) expressions in asthma are supposed as two mechanisms which associated with decreased COVID-19 susceptibility in asthmatics. Some studies have been performed to evaluate two mentioned factors in asthmatic patients compared with healthy individuals. Herein, we address these mechanisms and investigate whether ACE2 and eosinophil could protect asthmatic patients against SARS-CoV-2 infection.

The GNB3 c.825C>T (rs5443) polymorphism and protection against fatal outcome of corona virus disease 2019 (COVID-19).

Background and aims: Albeit several factors which influence the outcome of corona virus disease (COVID-19) are already known, genetic markers which may predict the outcome of the disease in hospitalized patients are still very sparse. Thus, in this study, we aimed to analyze whether the single-nucleotide polymorphism (SNP) rs5443 in the gene GNB3, which was associated with higher T cell responses in previous studies, might be a suitable biomarker to predict T cell responses and the outcome of COVID-19 in a comprehensive German cohort. Methods: We analyzed the influence of demographics, pre-existing disorders, laboratory parameters at the time of hospitalization, and GNB3 rs5443 genotype in a comprehensive cohort (N = 1570) on the outcome of COVID-19. In a sub cohort, we analyzed SARS-CoV-2-specific T cell responses and associated GNB3 rs5443 genotypes. We investigated the influence of all factors on COVID-19 fatality in multivariable analysis. Results: We found a younger patient age, normotension or absence of diabetes mellitus or cardiovascular diseases, normal blood cell counts, and low inflammatory markers at hospital admission were protective factors against fatal course of disease. In addition, the rs5443 TT genotype was significantly associated with protection against COVID-19 fatality (OR: 0.60, 95% CI: 0.40–0.92, p = 0.02). We also observed significantly increased SARS-CoV-2-specific T cell responses in rs5443 TT genotype carriers (p = 0.01). Although we observed a significant association of the factors described previously in univariate analysis, only a younger age of the patients, normal blood cell counts, and the GNB3 rs5443 TT genotype remained independent predictors against COVID-19 fatality in multivariable analysis. Conclusion: Immutable predictors for COVID-19 fatality are relatively rare. In this study we could show that the TT genotype of the SNP rs5443 in the gene GNB3 is associated with protection against COVID-19 fatality. It was as well correlated to higher SARS-CoV-2-specific T cell responses, which could result in a milder course of disease in those patients. Based on those observations we hereby provide a further prognostic biomarker, which might be used in routine diagnostics as a predictive factor for COVID-19 mortality already upon hospitalization.

Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning.

Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people’s daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis. To achieve this, a hybrid dataset was created employing publicly available sources comprising heterogeneous parameters from the majority of EU countries, e.g., mobility measures, policy responses, vaccinations, and demographics/generic country-level parameters. Data pre-processing and data exploration techniques were initially applied to normalize the available data and decrease the feature dimensionality of the data problem considered. Then, a linear ε-Support Vector Machine (ε-SVM) model was employed to implement the regression task of predicting the number of deaths for each one of the three first pandemic waves (with mean square error of 0.027 for wave 1 and less than 0.02 for waves 2 and 3). Post hoc explainability analysis was finally applied to uncover the rationale behind the decision-making mechanisms of the ML pipeline and thus enhance our understanding with respect to the contribution of the selected country-level parameters to the prediction of COVID-19 deaths in EU.

Association of latitude and altitude with adverse outcomes in patients with COVID-19: The VIRUS registry.

BACKGROUNDThe coronavirus disease 2019 (COVID-19) course may be affected by environmental factors. Ecological studies previously suggested a link between climatological factors and COVID-19 fatality rates. However, individual-level impact of these factors has not been thoroughly evaluated yet.AIMTo study the association of climatological factors related to patient location with unfavorable outcomes in patients.METHODSIn this observational analysis of the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study: COVID-19 Registry cohort, the latitudes and altitudes of hospitals were examined as a covariate for mortality within 28 d of admission and the length of hospital stay. Adjusting for baseline parameters and admission date, multivariable regression modeling was utilized. Generalized estimating equations were used to fit the models.RESULTSTwenty-two thousand one hundred eight patients from over 20 countries were evaluated. The median age was 62 (interquartile range: 49-74) years, and 54% of the included patients were males. The median age increased with increasing latitude as well as the frequency of comorbidities. Contrarily, the percentage of comorbidities was lower in elevated altitudes. Mortality within 28 d of hospital admission was found to be 25%. The median hospital-free days among all included patients was 20 d. Despite the significant linear relationship between mortality and hospital-free days (adjusted odds ratio (aOR) = 1.39 (1.04, 1.86), P = 0.025 for mortality within 28 d of admission; aOR = -1.47 (-2.60, -0.33), P = 0.011 for hospital-free days), suggesting that adverse patient outcomes were more common in locations further away from the Equator; the results were no longer significant when adjusted for baseline differences (aOR = 1.32 (1.00, 1.74), P = 0.051 for 28-day mortality; aOR = -1.07 (-2.13, -0.01), P = 0.050 for hospital-free days). When we looked at the altitude’s effect, we discovered that it demonstrated a non-linear association with mortality within 28 d of hospital admission (aOR = 0.96 (0.62, 1.47), 1.04 (0.92, 1.19), 0.49 (0.22, 0.90), and 0.51 (0.27, 0.98), for the altitude points of 75 MASL, 125 MASL, 400 MASL, and 600 MASL, in comparison to the reference altitude of 148 m.a.s.l, respectively. P = 0.001). We detected an association between latitude and 28-day mortality as well as hospital-free days in this worldwide study. When the baseline features were taken into account, however, this did not stay significant.CONCLUSIONOur findings suggest that differences observed in previous epidemiological studies may be due to ecological fallacy rather than implying a causal relationship at the patient level.

COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework

This research develops machine learning models equipped with interpretation modules for mortality risk prediction and stratification in cohorts of hospitalised coronavirus disease-2019 (COVID-19) patients with and without diabetes mellitus (DM). To this end, routinely collected clinical data from 156 COVID-19 patients with DM and 349 COVID-19 patients without DM were scrutinised. First, a random forest classifier forecasted in-hospital COVID-19 fatality utilising admission data for each cohort. For the DM cohort, the model predicted mortality risk with the accuracy of 82%, area under the receiver operating characteristic curve (AUC) of 80%, sensitivity of 80%, and specificity of 56%. For the non-DM cohort, the achieved accuracy, AUC, sensitivity, and specificity were 80%, 84%, 91%, and 56%, respectively. The models were then interpreted using SHapley Additive exPlanations (SHAP), which explained predictors’ global and local influences on model outputs. Finally, the k-means algorithm was applied to cluster patients on their SHAP values. The algorithm demarcated patients into three clusters. Average mortality rates within the generated clusters were 8%, 20%, and 76% for the DM cohort, 2.7%, 28%, and 41.9% for the non-DM cohort, providing a functional method of risk stratification.

COVID-19 Mortality in Europe, by Latitude and Obesity Status: A Geo-Spatial Analysis in 40 Countries.

On 30 January 2020, the World Health Organization (WHO) declared the current novel coronavirus disease 2019 (COVID-19) as a public health emergency of international concern and later characterized it as a pandemic. New data show that excess body mass and vitamin D deficiency might be related to the disease severity and mortality. The aim of this study was to evaluate whether latitude, as a proxy of sunlight exposure and Vitamin D synthesis, and prevalent obesity among European populations, is related to COVID-19 spread and severity. European COVID-19 data (incidence and fatality), including information on the prevalence of obesity, social distancing, and others were obtained by the “Our World in Data” website on 17 April 2021. Adjusted analysis showed that higher COVID-19 incidence and fatality were pictured in countries being in higher latitude, both during the whole period, as well as, during the time period 1 November 2020–31 March 2021. Higher incidence and fatality of COVID-19 were observed where the prevalence of overweight/obesity was higher during the whole time period, whereas during the time period 1 November 2020–31 March 2021, only COVID-19 incidence was higher but not a fatality. The present results provide insights for targeted interventions and preventive strategies against COVID-19.

Chronic Kidney Disease and COVID-19 Infection: A Case-Control Study.

To organize efforts to manage the coronavirus disease 2019 (COVID-19), it is necessary to understand which groups are at higher risk of infection. Kidney disease seems to be substantial in COVID-19 patients, but there are limited data on COVID-19 incidence and fatality among chronic kidney disease (CKD) patients. In this study, we intend to examine the association between CKD and susceptibility to COVID-19 infection. Participants were selected from those recruited in a population-based cross-sectional survey of CKD prevalence and associated risk factors in Iranian people 18 years and older. A three-part questionnaire was used for COVID-19 infection clinical symptoms and epidemiologic and hospitalization data. A total of 962 individuals including 403 CKD patients and 559 healthy controls were recruited in this study. Healthy controls were suffering more from common cold signs, cough, fever, sore throat, headache, anosmia, dyspnea, and abdominal pain (all P < 0.05). Furthermore, the number of healthy individuals with myalgia was marginally higher compared to the CKD patients (P = 0.057). Data regarding the number of CKD patients with/without COVID-19 infection throughout different CKD stages revealed that there was no significant difference between the two groups in terms of COVID-19 infection in different stages of CKD (P = 0.956). We found that some of the clinical presentations of COVID-19 including common cold symptoms, cough, fever, sore throat, headache, anosmia, dyspnea, and abdominal pain were higher among healthy individuals compared to the CKD group. On the other hand, the susceptibility to COVID-19 infection was not significantly different in various early stages of CKD.

Severity of COVID-19 patients with coexistence of asthma and vitamin D deficiency

Coronavirus disease 2019 (COVID-19)-driven global pandemic triggered innumerable health complications, imposing great challenges in managing other respiratory diseases like asthma. Furthermore, increases in the underlying inflammation involved in the fatality of COVID-19 have been linked with lack of vitamin D. In this research work, we intend to investigate the possible genetic linkage of asthma and vitamin D deficiency with the severity and fatality of COVID-19 using a network-based approach. We identified and analysed 41 and 14 differentially expressed genes (DEGs) of COVID-19 being common with asthma and vitamin D deficiency, respectively, through the comparative differential gene expression analysis and their footprints on signalling pathways. Gene set enrichment analysis for GO terms and signalling pathways reveals key biological activities, including inflammatory response-related pathways (e.g., cytokine- and chemokine-mediated signalling pathways, IL-17, and TNF signalling pathways). Besides, the Protein–Protein Interaction network analysis of those DEGs reveals hub proteins, some of which are reported as inflammatory antiviral interferon-stimulated biomarkers that potentially drive the cytokine storm leading to COVID-19 severity and fatality, and contributes in the early stage of viral replication, respectively. Moreover, the regulatory network analysis found these DEGs associated with antiviral and tumour inhibitory transcription factors and micro-RNAs. Finally, drug–target enrichment analysis yields tetradioxin, estradiol, arsenenous acid, and zinc, which have been reported to be effective in suppressing the pro-inflammatory cytokines production, and other respiratory tract infections. Our results yield shared biomarker-driven key hypotheses followed by network-based analytics, demystifying the mechanistic details of COVID-19 comorbidity of asthma and vitamin D deficiency with their potential therapeutic implications.

Postmortem findings in COVID-19 fatalities: A systematic review of current evidence

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing pandemic of coronavirus disease 2019 (COVID-19). Almost 17 months after the first COVID-19 case was reported, the exact pathogenesis of the virus is still open to interpretation. Postmortem studies have been relatively scarce due to the high infectivity rate of the virus. We systematically reviewed the literature available for studies that reported gross, histological, microscopic, and immunohistochemical findings in COVID-19 fatalities with the aim of reporting any recurrent findings among different demographics. PubMed and Scopus were searched up till the second of May 2021 and 46 studies with a total of 793 patients were shortlisted after the application of inclusion and exclusion criteria. The selected studies reported gross, histological, microscopic, and immunohistochemical autopsy findings in the lungs, heart, liver, gallbladder, bowels, kidney, spleen, bone marrow, lymph nodes, CNS, pancreas, endocrine/exocrine glands, and a few other miscellaneous locations. The SARS-CoV-2 virus was detected in multiple organs and so was the presence of widespread microthrombi. This finding suggests that the pathogenesis of this highly infectious virus might be linked to some form of coagulopathy. Further studies should focus on analyzing postmortem findings in a larger number of patients from different demographics in order to obtain more generalizable results.

COVID-19 Is a Multi-Organ Aggressor: Epigenetic and Clinical Marks.

The progression of coronavirus disease 2019 (COVID-19), resulting from a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, may be influenced by both genetic and environmental factors. Several viruses hijack the host genome machinery for their own advantage and survival, and similar phenomena might occur upon SARS-CoV-2 infection. Severe cases of COVID-19 may be driven by metabolic and epigenetic driven mechanisms, including DNA methylation and histone/chromatin alterations. These epigenetic phenomena may respond to enhanced viral replication and mediate persistent long-term infection and clinical phenotypes associated with severe COVID-19 cases and fatalities. Understanding the epigenetic events involved, and their clinical significance, may provide novel insights valuable for the therapeutic control and management of the COVID-19 pandemic. This review highlights different epigenetic marks potentially associated with COVID-19 development, clinical manifestation, and progression.

Healthcare under pressure: modelling COVID-19 fatalities with multiscale geographically weighted regressions

PurposeThe paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective.Design/methodology/approachMultiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany.FindingsThe authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development.Originality/valueThis research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination.