Progression Of COVID-19 Research Articles

Life cycle of COVID-19 Progression in a Given Region

The study focuses on revealing the typical patterns of COVID-19 spread. The identification of regularities is based on conceptual processing data, in which a sample is approximated by model functions expressing a specific phenomenological concept of the process in question. The reconstruction of desired parameters is hindered by insufficient information on the growth features, the limited volume of the required experimental data, and incomplete elucidation of the phenomenological properties of the objects required to formulate complex models. The investigation addresses these limitations using the Verhulst equation, which expresses the general factors of object growth and adequately describes the growth dynamics in the context of significant theoretical uncertainties regarding an object under study. The need for identifiability and numerical stability of the model parameter estimates is also addressed, and a special regularization approach is applied to obtain an adequate and stable picture of the virus’s evolutionary dynamics in the context of significant uncertainties. The results demonstrate that the growth of the viral population can be integrally described by two variable parameters of the Verhulst equation. Interpreting the reconstructed functions indicates the existence of the typical pattern of seven stages of COVID-19 spread. The possibility of radically managing the number of infected persons is established. The proposed processing of coronavirus cases is of practical value for the preclinical diagnosis and control of virus spread in any geographic area, such as a district, city, local region, or country.

MODERN DIRECTED ANTIVIRAL COVID-19 THERAPY: RESULTS OF MULTICENTER CLINICAL EFFECTIVENESS AND SAFETY STUDY OF FIXED NIRMATRELVIR+RITONAVIR COMBINATION

The article presents the data from an open, two-stage, multicenter study on the efficacy and safety evaluation of a combined drug (a fixed combination of nirmatrelvir 300 mg and ritonavir 100 mg) in the complex therapy in COVID-19 patients.The aim of the study was to assess the safety, tolerability and pharmacokinetic parameters of the fixed combination of nirmatrelvir 300 mg and ritonavir 100 mg in healthy volunteers, the efficacy and safety assessment of the drug in the combination therapy compared with the standard therapy in COVID-19 patients.Material and methods. An open two-stage multicenter clinical study to assess the main pharmacokinetic parameters, safety, and efficacy against COVID-19 of the drug nirmatrelvir 300 mg and ritonavir 100 mg combination (Skyvira® PROMOMED RUS LLC, Russia) in the adult population, included 2 stages. At stage 1, safety, tolerability and pharmacokinetic parameters were evaluated in healthy volunteers (over 18 years of age) in order to confirm their comparability with the literature data known for a set of active substances. Phase 2 assessed efficacy and safety in COVID-19 patients. As a part of the second stage, the study involved 264 patients (men and women aged 18 to 80 years), who had been divided into two groups. The first group patients (n=132) received the study drugs (nirmatrelvir 300 mg and ritonavir 100 mg) – 1 tablet twice a day with an interval of 12±2 hours for 5 days in combination with pathogenetic and symptomatic therapy. The second group patients (n=132) received standard therapy in accordance with the approved Temporary Guidelines for the Prevention and Treatment of Novel Coronavirus Infection (Version 15 dated February 22, 2022).Results. During the study, none of the patients from the (nirmatrelvir + ritonavir) group experienced a transition of the COVID-19 course to a heavier severity level, in contrast to the patients in the standard therapy group. The study participants included patients with comorbidities (68% of the general population), with risk factors for COVID-19 progression to a heavier severity level and the risk of hospitalization (75% of the general population). There were no cases of COVID-19 progression to a heavier severity level in the study drug group. By the 6th day, in the nirmatrelvir + ritonavir group, the proportion of the patients who had achieved a complete recovery was twice more and amounted to 35.61% (p=0.0001), and the proportion of the patients with a negative RNA analysis to SARS-CoV-2 was 20% higher than in the comparison group, and amounted to 82.58% (p=0.0001). The fixed nirmatrelvir + ritonavir combination therapy has a favorable safety profile comparable to the standard therapy. The identified adverse reactions were transient in nature and did not require discontinuation of therapy or changes in the treatment regimen.Conclusion. The fixed nirmatrelvir + ritonavir combination has a favorable safety profile in COVID-19 patients, comparable to the standard therapy. The data obtained demonstrate a clinical and pharmacoeconomic feasibility of including the fixed (nirmatrelvir + ritonavir) combination in the COVID-19 treatment regimen.

Vitamin D Level in Laboratory Confirmed COVID-19 and Disease Progression.

There is no conclusive evidence to suggest vitamin D level can prevent or treat infection with the new coronavirus disease 2019. This study aimed to investigate the effects of serum level of vitamin D in patients with coronavirus disease 2019 on death, severity, and hospitalization duration. Baseline characteristic of patients was extracted from the Isfahan coronavirus disease 2019 registry database (I-CORE). Blood samples were taken from all patients to measure the level of vitamin D (25-hydroxyvitamin D) and categorized. The effect of 25(OH) D on death, severity, and hospitalization duration was analyzed by logistic regression. Among our study patients, 5.5% had a severe deficiency of vitamin D, 23.7% deficiency, and 24.8% insufficiency. Of the 107 patients who died, 7.5% were severely deficient in vitamin D. We found that vitamin D deficiency had no significant effect on death, disease severity, and hospitalization (P > .05). However, having at least one comorbidity increased the odds of death five times after adjusting age > 60 years and gender (P < .0001). The results showed that among all comorbidities, diabetes has the greatest impact on the outcomes as it raised the odds of death, disease severity, and length of hospital stay by 2.23,1.72, and 1.48, respectively, after controlling the age > 60 and gender (P = .0002, P=.08, P=.012). The mortality, disease severity, and hospitalization of coronavirus disease 2019 patients seem to be not affected by the low levels of 25(OH)D. However, the synergy between vitamin D levels and comorbidities, age, and gender could affect the outcome of coronavirus disease 2019 patients.

Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework.

We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10729-022-09619-y.

A novel plot for the early alert of epidemic growth using regional targets: the doubling plot

Abstract Background During the pandemic, restrictions set by the Italian Government were primarily based on the regional level of key parameters including hospitalization and incidence rates. We aimed to build a specific plot to monitor trends and trigger early alerts, with daily updates publicly available on a National Portal. Methods A multidisciplinary team conceived and implemented a new composite plot, developing ad hoc R scripts on top of a specialised database, built in collaboration with the Ministry of Health. We calculated the doubling time Td as log(2)/log(1+r/100), where r is the daily change of target parameters and Td ranges between (0,+-infinity), and not determined for constant or missing values. We calculated Td daily, as either doubling (growth) or halving (decrease) time. To visualize trends, we assembled two different types of graphs: a bivariate plot showing the path of each point (Td, target parameter) over time, and a line plot of Td over time. The Y axis was inverted for doubling times, as lower Td indicate higher alert in this case. The two graphs were arranged in lines, using cutoffs for excessive high values for doubling times and low values for halving times. A third line was included to display trends of the target parameter over time. Results The plot was successfully realized and published on the Portal for all regions in February 2021 (https://www.agenas.gov.it/covid19/web/index.php?r=english%2Fdoubling&amp;q=ITA&amp;t=0). Since July 2021, we used the doubling plot to monitor the three main parameters adopted to set restrictions for Covid-19: a) occupancy rates in intensive care; b) occupancy rates in medical wards; c) weekly incidence rates. The plot highlighted growth trends and early alerts, particularly in the initial phases of growth. Conclusions The doubling plot can provide useful information to trigger early responses for pandemic control in decentralised governance. R code is available open source from AGENAS for free use. Key messages • The doubling plot was conceived and implemented on a National Portal to trigger early alerts of Covid-19 progression in Italian Regions and Autonomous Provinces. • The plot could be rapidly adapted to legislative parameters and can be useful in different situations to monitor epidemic growth and support public health policies.

Lessons learned and implications of early therapies for coronavirus disease in a territorial service centre in the Calabria region: a retrospective study

BackgroundMonoclonal antibodies (mAbs) and antivirals have been approved for early therapy of coronavirus disease (COVID-19), however, in the real-life setting, there are difficulties to prescribe these therapies within few days from symptom onset as recommended, and effectiveness of combined use of these drugs have been hypothesised in most-at-risk patients (such as those immunocompromised) but data supporting this strategy are limited.MethodsWe describe the real-life experience of SARS-CoV-2 antivirals and/or monoclonal antibodies (mAbs) and focus on the hospitalisation rate due to the progression of COVID-19. Clinical results obtained through our risk-stratification algorithm and benefits achieved through a strategic proximity territorial centre are provided. We also report a case series with an in-depth evaluation of SARS-CoV-2 genome in relationship with treatment strategy and clinical evolution of patients.ResultsTwo hundred eighty-eight patients were analysed; 94/288 (32.6%) patients were treated with mAb monotherapy, 171/288 (59.4%) patients were treated with antivirals, and 23/288 (8%) patients received both mAbs and one antiviral drug. Haematological malignancies were more frequent in patients treated with combination therapy than in the other groups (p = 0.0003). There was a substantial increase in the number of treated patients since the opening of the centre dedicated to early therapies for COVID-19. The provided disease-management and treatment appeared to be effective since 98.6% patients recovered without hospital admission. Moreover, combination therapy with mAbs and antivirals seemed successful because all patients admitted to the hospital for COVID-19 did not receive such therapies, while none of the most-at-risk patients treated with combination therapy were hospitalized or reported adverse events.ConclusionsA low rate of COVID-19 progression requiring hospital admission was observed in patients included in this study. The dedicated COVID-19 proximity territorial service appeared to strengthen the regional sanitary system, avoiding the overwhelming of other services. Importantly, our results also support early combination therapy: it is possible that this strategy reduces the emergence of escape mutants of SARS-CoV-2, thereby increasing efficacy of early treatment, especially in immunocompromised individuals.

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases.

Simple SummaryFor the last two years, COVID-19 has been rigorously studied aiming to identify novel prognostic and therapeutic avenues. Recently, T cell receptor profiling has emerged as a method to associate adaptive immunity with COVID-19 progression and severity. Such data are typically analyzed to explore T cell receptor properties and characteristics in the context of SARS-CoV-2 infection. However, the equally informative alternative analytic strategy of identifying any preferential recognition of viral antigens by the T-cell-mediated immune response is mostly overlooked. In this study, we propose a novel Machine-Learning-oriented approach for analyzing T cell receptor repertoires that is based on the concept of utilising the level at which each SARS-CoV-2 antigen is recognised by the available T cell receptors in each sample from COVID-19-convalescent and healthy cohorts. This approach also allowed us to observe a group of T cell receptors capable of recognising SARS-CoV-2 antigens that were already established in samples from the healthy cohort, leading us to the cross-reactivity phenomenon hypothesis. To explore this, all T cell receptors were examined for being able to recognise antigens from other pathogens and diseases, unveiling evidence of putative cross-reactivity with M. tuberculosis and Influenza virus, among others.During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community’s focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens’ point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

Host Genetic Risk Factors Associated with COVID-19 Susceptibility and Severity in Vietnamese.

Since the emergence and rapid transmission of SARS-CoV-2, numerous scientific reports have searched for the association of host genetic variants with COVID-19, but the data are mostly acquired from Europe. In the current work, we explored the link between host genes (SARS-CoV-2 entry and immune system related to COVID-19 sensitivity/severity) and ABO blood types with COVID-19 from whole-exome data of 200 COVID-19 patients and 100 controls in Vietnam. The O blood type was found to be a protective factor that weakens the worst outcomes of infected individuals. For SARS-CoV-2 susceptibility, rs2229207 (TC genotype, allele C) and rs17860118 (allele T) of IFNAR2 increased the risk of infection, but rs139940581 (CT genotype, allele T) of SLC6A20 reduced virus sensitivity. For COVID-19 progress, the frequencies of rs4622692 (TG genotype) and rs1048610 (TC genotype) of ADAM17 were significantly higher in the moderate group than in the severe/fatal group. The variant rs12329760 (AA genotype) of TMPRSS2 was significantly associated with asymptomatic/mild symptoms. Additionally, rs2304255 (CT genotype, allele T) of TYK2 and rs2277735 (AG genotype) of DPP9 were associated with severe/fatal outcomes. Studies on different populations will give better insights into the pathogenesis, which is ethnic-dependent, and thus decipher the genetic factor’s contribution to mechanisms that predispose people to being more vulnerable to COVID-19.

Simultaneous quantification of nirmatrelvir and ritonavir by LC-MS/MS in patients treated for COVID-19

Nirmatrelvir is an antiviral agent active against SARS-CoV-2, the virus causing the pandemic disease COVID-19. It is administrated in combination with the protease inhibitor ritonavir, which acts in case of COVID-19 mainly as enzyme blocking agent preventing the premature metabolic elimination of nirmatrelvir. The combination of the two drugs in separate tablets is marketed under the brand name Paxlovid® and shows good effectivity in preventing the progression of COVID-19 to severe disease state. In this work, we described a LC-MS/MS method for the simultaneous quantification of nirmatrelvir and ritonavir in human plasma of patients treated for COVID-19 with Paxlovid®. After addition of D6-ritonavir as internal standard, plasma proteins were precipitated by the addition of methanol. The analytes were separated by gradient elution on a C18-column and were detected by tandem mass spectrometry. Calibration functions were linear in the ranges of 10 – 10000 ng/mL for nirmatrelvir and 2 – 2000 ng/mL for ritonavir. Inter-day and intra-day precision and accuracy was better than 15 % in the quality control samples and better than 20 % at the LLOQ. The method was successfully applied on samples of hospitalized patients treated for COVID-19 and proved to be capable in supporting therapeutic drug monitoring (TDM).

Label-free electrochemical immunosensor for highly sensitive COVID-19 spike protein detection

The ongoing coronavirus pandemic responsible for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly increased the rate of global death and infections due to variant mutations (such as Delta and Omicron). While specifically developed and approved vaccines can limit the spread of disease in a population and severity of resulting symptoms, none have been demonstrated to effectively prevent infection altogether. Thus, reliable early diagnosis of COVID-19 is critical to identify positive cases to help contain the outbreak. Herein we report a label-free electrochemical immunosensor for rapid diagnosis of COVID-19 by using nitrogen-doped holey graphene (N-HRGO) as a nanocarrier decorated with thionine (TH) molecules as electrochemical indicators. With the spike protein located on the surface of the COVID-19 particles as the model target, the as-prepared electrochemical immunosensor could detect the presence of the COVID-19 spike protein over a wide linear range (1 pg mL−1−10 ng mL−1) with a low detection limit (0.3 pg mL−1). In addition, the developed electrochemical immunosensor exhibited an excellent selectivity (with insignificant current changes towards interfering proteins comparing with COVID-19 spike protein), a good reproducibility and long-term storage stability. Importantly, the electrochemical immunosensor thus developed could successfully and reliably detect the spike protein of COVID-19 in saliva and human serum complex samples. Thus, the as-prepared label-free electrochemical immunosensor can achieve rapid and sensitive detection of the COVID-19 spike protein, as a promising clinical diagnosis tool in monitoring the progression of COVID-19.

Association of smoking type, status, number times of smoking and their effect on infected people by COVID-19

Background: Smoking has a significant role in weakening the pulmonary immune system function. Infection with infectious agents can cause more serious outcomes in the pulmonary system among people who smoke than those who do not.&#x0D; Objective: To show the relationship between smoking and the progression of the infectious COVID-19.&#x0D; Patients and Methods: A questionnaire the following data was gathered from both sexes using a Google form: age, smoking status, smoking habits, blood type, and medical conditions.&#x0D; Results: Results: Statistical analysis indicated that there are highly significant differences between males and females, and older patients included in the study who are infected and non-infected with COVID-19 (P &lt; 0.01), and there are significant differences among blood groups (P &lt; 0.05). In regard to smoking status, there are highly significant differences in the disease severity between the never-smoking group compared to current smokers (P≤0.01, P≤0.05), respectively. This differences increase with the number of times of smoking, and there is no significant difference between types of smoking between infected and non-infected groups with COVID-19. Statistical analysis indicated that there are highly significant differences in the disease severity in the male and female groups who have not smoked compared to former smokers (P &lt; 0.01).&#x0D; Conclusion: Recent studies have reported the real role of the virus in the health status of smokers. However, this study showed no strong relationship between the virus and smokers.

Association of smoking type, status, number times of smoking and their effect on infected people by COVID-19

Background: Smoking has a significant role in weakening the pulmonary immune system function. Infection with infectious agents can cause more serious outcomes in the pulmonary system among people who smoke than those who do not. Objective: To show the relationship between smoking and the progression of the infectious COVID-19. Patients and Methods: A questionnaire the following data was gathered from both sexes using a Google form: age, smoking status, smoking habits, blood type, and medical conditions. Results: Results: Statistical analysis indicated that there are highly significant differences between males and females, and older patients included in the study who are infected and non-infected with COVID-19 (P &lt; 0.01), and there are significant differences among blood groups (P &lt; 0.05). In regard to smoking status, there are highly significant differences in the disease severity between the never-smoking group compared to current smokers (P≤0.01, P≤0.05), respectively. This differences increase with the number of times of smoking, and there is no significant difference between types of smoking between infected and non-infected groups with COVID-19. Statistical analysis indicated that there are highly significant differences in the disease severity in the male and female groups who have not smoked compared to former smokers (P &lt; 0.01). Conclusion: Recent studies have reported the real role of the virus in the health status of smokers. However, this study showed no strong relationship between the virus and smokers.

Limited value of neutrophil-to-lymphocyte ratio and serum creatinine as point-of-care biomarkers of disease severity and infection mortality in patients hospitalized with COVID-19.

In hospitalized COVID-19, neutrophil-to-lymphocyte ratio (NLR) and serum creatinine is sometimes measured under assumption they predict disease severity and mortality. We determined the potential value of NLR and serum creatinine as predictors of disease severity and mortality in COVID-19. Prospective cohort study of COVID-19 patients admitted to premier COVID-19 treatment hospitals in Ethiopia. Predictive capability of biomarkers in progression and prognosis of COVID-19 was analyzed using receiver operating characteristics. Survival of COVID-19 patients with different biomarker levels was computed. Logistic regression assessed associations between disease severity and mortality on NLR and serum creatinine adjusted for odds ratio (AOR). The study enrolled 126 adults with severe (n = 68) or mild/moderate (n = 58) COVID-19, with median age 50 [interquartile range (IQR 20-86)]; 57.1% males. The NLR value was significantly higher in severe cases [6.68 (IQR 3.03-12.21)] compared to the mild/moderate [3.23 (IQR 2.09-5.39)], with the NLR value markedly associated with disease severity (p<0.001). Mortality was higher in severe cases [13 (19.1%)] compared to mild/moderate cases [2 (3.4%)] (p = 0.007). The NLR value was significantly higher in non-survivors [15.17 (IQR 5.13-22.5)] compared to survivors [4.26 (IQR 2.40-7.90)] (p = 0.002). Serum creatinine was significantly elevated in severe cases [34 (50%)] compared with mild/moderate [11 (19%)] (p<0.001). Disease severity [AOR 6.58, 95%CI (1.29-33.56), p = 0.023] and NLR [AOR 1.07, 95%CI (1.02-1.12), p = 0.004)] might be associated with death. NLR had a sensitivity and specificity of 69.1% and 60.3% as predictor of disease severity (cut-off >4.08), and 86.7% and 55.9% as prognostic marker of mortality (cut-off >4.63). In COVID-19, NLR is a biomarker with only modest accuracy for predicting disease severity and mortality. Still, patients with NLR >4.63 are more likely to die. Monitoring of this biomarker at the earliest stage of the disease may predict outcome. Additionally, high creatinine seems related to disease severity and mortality.

Circulating multimeric immune complexes contribute to immunopathology in COVID-19.

A dysregulated immune response with high levels of SARS-CoV-2 specific IgG antibodies characterizes patients with severe or critical COVID-19. Although a robust IgG response is considered to be protective, excessive triggering of activating Fc-gamma-receptors (FcγRs) could be detrimental and cause immunopathology. Here, we document excessive FcγRIIIA/CD16A activation in patients developing severe or critical COVID-19 but not in those with mild disease. We identify two independent ligands mediating extreme FcγRIIIA/CD16A activation. Soluble circulating IgG immune complexes (sICs) are detected in about 80% of patients with severe and critical COVID-19 at levels comparable to active systemic lupus erythematosus (SLE) disease. FcγRIIIA/CD16A activation is further enhanced by afucosylation of SARS-CoV-2 specific IgG. Utilizing cell-based reporter systems we provide evidence that sICs can be formed prior to a specific humoral response against SARS-CoV-2. Our data suggest a cycle of immunopathology driven by an early formation of sICs in predisposed patients. These findings suggest a reason for the seemingly paradoxical findings of high antiviral IgG responses and systemic immune dysregulation in severe COVID-19. The involvement of circulating sICs in the promotion of immunopathology in predisposed patients opens new possibilities for intervention strategies to mitigate critical COVID-19 progression.

Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma

BackgroundThe coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.MethodsTwo sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.ResultsWe discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.ConclusionWe identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.

Metabolomics analysis identifies glutamic acid and cystine imbalances in COVID-19 patients without comorbid conditions. Implications on redox homeostasis and COVID-19 pathophysiology.

It is well known that the presence of comorbidities and age-related health issues may hide biochemical and metabolic features triggered by SARS-CoV-2 infection and other diseases associated to hypoxia, as they are by themselves chronic inflammatory conditions that may potentially disturb metabolic homeostasis and thereby negatively impact on COVID-19 progression. To unveil the metabolic abnormalities inherent to hypoxemia caused by COVID-19, we here applied gas chromatography coupled to mass spectrometry to analyze the main metabolic changes exhibited by a population of male patients less than 50 years of age with mild/moderate and severe COVID-19 without pre-existing comorbidities known to predispose to life-threatening complications from this infection. Several differences in serum levels of particular metabolites between normal controls and patients with COVID-19 as well as between mild/moderate and severe COVID-19 were identified. These included increased glutamic acid and reduced glutamine, cystine, threonic acid, and proline levels. In particular, using the entire metabolomic fingerprint obtained, we observed that glutamine/glutamate metabolism was associated with disease severity as patients in the severe COVID-19 group presented the lowest and higher serum levels of these amino acids, respectively. These data highlight the hypoxia-derived metabolic alterations provoked by SARS-CoV-2 infection in the absence of pre-existing co-morbidities as well as the value of amino acid metabolism in determining reactive oxygen species recycling pathways, which when impaired may lead to increased oxidation of proteins and cell damage. They also provide insights on new supportive therapies for COVID-19 and other disorders that involve altered redox homeostasis and lower oxygen levels that may lead to better outcomes of disease severity.

Low SARS-CoV-2 viral load among vaccinated individuals infected with Delta B.1.617.2 and Omicron BA.1.1.529 but not with Omicron BA.1.1 and BA.2 variants.

The rapid spread of SARS-CoV-2 variants in the global population is indicative of the development of selective advantages in emerging virus strains. Here, we performed a case-control investigation of the clinical and demographic characteristics, clinical history, and virological markers to predict disease progression in hospitalized adults for COVID-19 between December 2021 and January 2022 in Chennai, India. COVID-19 diagnosis was made by a commercial TaqPath COVID-19 RT-PCR, and WGS was performed with the Ion Torrent Next Generation Sequencing System. High-quality (<5% of N) complete sequences of 73 Omicron B.1.1.529 variants were randomly selected for phylogenetic analysis. SARS-CoV-2 viral load, number of comorbidities, and severe disease presentation were independently associated with a shorter time-to-death. Strikingly, this was observed among individuals infected with Omicron BA.2 but not among those with the BA.1.1.529, BA.1.1, or the Delta B.1.617.2 variants. Phylogenetic analysis revealed severe cases predominantly clustering under the BA.2 lineage. Sequence analyses showed 30 mutation sites in BA.1.1.529 and 33 in BA.1.1. The mutations unique to BA.2 were T19I, L24S, P25del, P26del, A27S, V213G, T376A, D405N and R408S. Low SARS-CoV-2 viral load among vaccinated individuals infected with Delta B.1.617.2 and the Omicron BA.1.1.529 variant but not with Omicron BA.1.1 or BA.2 suggests that the newer strains are largely immune escape variants. The number of vaccine doses received was independently associated with increased odds of developing asymptomatic disease or recovery. We propose that the novel mutations reported herein could likely bear a significant impact on the clinical characteristics, disease progression, and epidemiological aspects of COVID-19. Surging rates of mutations and the emergence of eclectic variants of SARS-CoV-2 appear to impact disease dynamics.

Building local decision-making competencies during COVID-19: Accelerating the transition from learning healthcare systems to learning health communities.

IntroductionThe persisting and evolving COVID‐19 pandemic has made apparent that no singular policy of mitigation at a regional, national or global level has achieved satisfactory and universally acceptable results. In the United States, carefully planned and executed pandemic policies have been neither effective nor popular and COVID‐19 risk management decisions have been relegated to individual citizens and communities. In this paper, we argue that a more effective approach is to equip and strengthen community coalitions to become local learning health communities (LLHCs) that use data over time to make adaptive decisions that can optimize the equity and well‐being in their communities.MethodsWe used data from the North Carolina (NC) county and zip code levels from May to August 2020 to demonstrate how a LLHC could use statistical process control (SPC) charts and simple statistical analysis to make local decisions about how to respond to COVID‐19.ResultsWe found many patterns of COVID‐19 progression at the local (county and zip code) levels during the same time period within the state that were completely different from the aggregate NC state level data used for policy making.ConclusionsSystematic approaches to learning from local data to support effective decisions have promise well beyond the current pandemic. These tools can help address other complex public health issues, and advance outcomes and equity. Building this capacity requires investment in data infrastructure and the strengthening of data competencies in community coalitions to better interpret data with limited need for advanced statistical expertise. Additional incentives that build trust, support data transparency, encourage truth‐telling and promote meaningful teamwork are also critical. These must be carefully designed, contextually appropriate and multifaceted to motivate citizens to create and sustain an effective learning system that works for their communities.

Uric acid as a prognostic predictor in COVID-19.

The purpose of our study was to investigate the incidence and prognostic significance of baseline and control uric acid values in COVID-19. The study population included patients admitted with the diagnosis of SARS-CoV-2 between March 2020 and March 2021. The demographic data, clinical, laboratory, and radiological findings were recorded. Uric acid levels were measured at the time of admission for 498 patients and at the most severe period of the disease in 143 patients. Length of hospital stay, need for admission to intensive care unit, the course, and outcomes during hospitalization were recorded. The mean age of 261 male and 207 female patients was 62.7(21-95) years. At the time of admission, 21 patients had hypouricemia and 170 had hyperuricemia. The need for ICU was 47.6% in the hypouricemic, 19.2% in the normouricemic, and 21.2% in the hyperuricemic groups. The mean uric acid level was 5.24±2.54 mg/dl in patients who required ICU admission and 5.18±1.98 mg/dl in patients who were discharged from the ward. The difference was not statistically significant. The mean uric acid level was not significantly different in the deceased and survivors. In 143 subjects, uric acid levels were measured after the progression of COVID-19; 73 of them were admitted to the ICU. The mean uric acid levels were found to be significantly decreased in patients with a negative prognosis. In our study, hypouricemia was not found to be a major feature of SARS-CoV-2 infection. Low baseline uric acid levels were associated with increased ICU admission. The decline in uric acid levels during hospital stay predicted poor prognosis, as well.

Modeling nonlocal behavior in epidemics via a reaction–diffusion system incorporating population movement along a network

The outbreak of COVID-19, beginning in 2019 and continuing through the time of writing, has led to renewed interest in the mathematical modeling of infectious disease. Recent works have focused on partial differential equation (PDE) models, particularly reaction–diffusion models, able to describe the progression of an epidemic in both space and time. These studies have shown generally promising results in describing and predicting COVID-19 progression. However, people often travel long distances in short periods of time, leading to nonlocal transmission of the disease. Such contagion dynamics are not well-represented by diffusion alone. In contrast, ordinary differential equation (ODE) models may easily account for this behavior by considering disparate regions as nodes in a network, with the edges defining nonlocal transmission. In this work, we attempt to combine these modeling paradigms via the introduction of a network structure within a reaction–diffusion PDE system. This is achieved through the definition of a population-transfer operator, which couples disjoint and potentially distant geographic regions, facilitating nonlocal population movement between them. We provide analytical results demonstrating that this operator does not disrupt the physical consistency or mathematical well-posedness of the system, and verify these results through numerical experiments. We then use this technique to simulate the COVID-19 epidemic in the Brazilian region of Rio de Janeiro, showcasing its ability to capture important nonlocal behaviors, while maintaining the advantages of a reaction–diffusion model for describing local dynamics.