COVID-19: Why does disease severity vary among individuals?

Occurrence and clinical correlates of SARS-CoV-2 viremia in two German patient cohorts

Viremia defined as detectable SARS-CoV-2 RNA in the blood is a potential marker of disease severity and prognosis in COVID-19 patients. Here, we determined the frequency of viremia in serum of two independent COVID-19 patient cohorts within the German National Pandemic Cohort Network (German: Nationales Pandemie Kohorten Netzwerk, NAPKON) with diagnostic RT-PCR against SARS-CoV-2. A cross-sectional cohort with 1122 COVID-19 patients (German: Sektorenuebergreifende Platform, SUEP) and 299 patients recruited in a high-resolution platform with patients at high risk to develop severe courses (German: Hochaufloesende Plattform, HAP) were tested for viremia. Our study also involved a comprehensive analysis and association of serological, diagnostic, and clinical parameters of the NAPKON medical dataset. Prevalence of viremia at the recruitment visit was 12.8% (SUEP) and 13% (HAP), respectively. Serological analysis revealed that viremic patients had lower levels of SARS-CoV-2 specific antibodies as well as lower neutralizing antibodies compared to aviremic patients. Viremia was associated with severity (<0.0001 SUEP; 0.002 HAP) and mortality of COVID-19 (both cohorts <0.0001) compared to aviremic patients. While rare, viremia was also detected in patients with mild disease (0.7%). In patients of the SUEP cohort with acute kidney disease (p = 0.0099) and hematooncological conditions (p = 0.0091), viremia was detected more frequently. Compared to the aviremic group, treatment with immunomodulating drugs as well as elevated levels of inflammatory markers in the blood was more frequent in the viremic group. In conclusion, our analysis revealed that detectable viremia correlates with hyperinflammatory conditions and higher risk for severe COVID-19 disease.

Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA

COVID-19, severe acute respiratory syndrome coronavirus 2, rapidly spread worldwide. Severe and critical patients are expected to rapidly deteriorate. Although several studies have attempted to uncover the mechanisms underlying COVID-19 severity, most have focused on the perturbations of single genes. However, the complex mechanism of COVID-19 involves numerous perturbed genes in a molecular network rather than a single abnormal gene. Thus, we aimed to identify COVID-19 severity-specific markers in the Japanese population using gene network analysis. In order to reveal the severity-specific molecular interplays, we developed a novel computational network biology strategy that measures dissimilarity between networks based on the comprehensive information of gene network (i.e., expression levels of genes and network structure) by using Kullback-Leibler divergence. Monte Carlo simulations demonstrated the effectiveness of our strategy for differential gene network analysis. We applied this method to publicly available whole blood RNA-seq data from the Japan coronavirus disease 2019 Task Force and identified differentially regulated molecular interplays between 368 severe and 105 non-severe samples. Our analysis suggests the gene network between HLA class II, CIITA, and CD74 as a COVID-19 severity specific molecular marker. Although the association between HLA class II and COVID-19 has been demonstrated, our data analysis revealed that the molecular interplay of HLA class II with its target and/or regulator is a crucial marker for COVID-19 severity. Our findings from computational network biology analysis suggest that suppression and activation of the molecular interplay between HLA class II, CIITA, and CD74 provide crucial clues to uncover the mechanisms of COVID-19 severity.

Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study

BACKGROUND

Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus complicating the prediction of clinical outcomes for different severe acute respiratory syndrome coronavirus (SARS-CoV-2) variants. Although millions of SARS-CoV-2 genomes have been publicly shared in global databases, linkages with detailed clinical data are scarce. Therefore, we aimed to establish a COVID-19 patient dataset with linked clinical and viral genomic data to then examine associations between SARS-CoV-2 genomic signatures and clinical disease phenotypes.

METHODS

A cohort of adult patients with laboratory confirmed SARS-CoV-2 from 11 participating healthcare institutions in the Greater Toronto Area (GTA) were recruited from March 2020 to April 2022. Supervised machine learning (ML) models were developed to predict hospitalization using SARS-CoV-2 lineage-specific genomic signatures, patient demographics, symptoms, and pre-existing comorbidities. The relative importance of these features was then evaluated.

RESULTS

Complete clinical data and viral whole genome level information were obtained from 617 patients, 50.4% of whom were hospitalized. Notably, inpatients were older with a mean age of 66.67 years (SD ± 17.64 years), whereas outpatients had a mean age of 44.89 years (SD ± 16.00 years). SHapley Additive exPlanations (SHAP) analyses revealed that underlying vascular disease, underlying pulmonary disease, and fever were the most significant clinical features associated with hospitalization. In models built on the amino acid sequences of functional regions including spike, nucleocapsid, ORF3a, and ORF8 proteins, variants preceding the emergence of variants of concern (VOCs) or pre-VOC variants, were associated with hospitalization.

CONCLUSIONS

Viral genomic features have limited utility in predicting hospitalization across SARS-CoV-2 diversity. Combining clinical and viral genomic datasets provides perspective on patient specific and virus-related factors that impact COVID-19 disease severity. Overall, clinical features had greater discriminatory power than viral genomic features in predicting hospitalization.

The Role of Inflammation in the Pathogenesis of Viral Respiratory Infections

Viral respiratory infections (VRIs) are a leading cause of morbidity and mortality worldwide, making them a significant public health concern. During infection, respiratory viruses, including Influenza virus, SARS-CoV-2, and respiratory syncytial virus (RSV), trigger an antiviral immune response, specifically boosting the inflammatory response that plays a critical role in their pathogenesis. The inflammatory response induced by respiratory viruses can be a double-edged sword since it can be initially induced to be antiviral and protective/reparative from virus-induced injuries. Still, it can also be detrimental to host cells and tissues. However, the mechanisms that differentiate the complex crosstalk between favorable host inflammatory responses and harmful inflammatory responses are poorly understood. This review explores the complex interplay between viral pathogens and the host immune response, mainly focusing on the role of inflammation in the pathogenesis of VRIs. We discuss how inflammation can both contain and exacerbate the progression of viral infections, highlighting potential therapeutic targets and emerging drugs for modulating the aberrant inflammatory responses during VRIs.

Clinical, biochemical, and genetic study of TACE/TNF-α/ACE signaling pathway in pediatric COVID-19 infection

BACKGROUND

Pediatric patients infected with coronavirus disease 2019 (COVID-19) have unique clinical characteristics. Tumor necrosis factor (TNF) is a proinflammatory cytokine that greatly contributes to tumor pathogenesis.

PURPOSE

To describe the presenting characteristics of COVID-19 infection among pediatric patients, and investigate the possible role of the TNF-α signaling pathway.

METHODS

This prospective case-control study included 50 Egyptian pediatric patients with COVID-19 and 50 healthy controls. Clinical, laboratory, and radiological assessments were performed. Serum TNF-alpha (TNF-α), TNF-α-converting enzyme (TACE), and angiotensin-converting enzyme 2 (ACE2) were measured using enzyme-linked immunosorbent assay. ACE (I/D) (rs4646994), ACE2 rs2285666, and TNF-α-308G/A single nucleotide polymorphisms (SNPs) were performed using conventional polymerase chain reaction techniques with or without restriction fragment length polymorphism.

RESULTS

The median age was 1 year (interquartile range [IQR], 0.31-2.50 years) in the case group and 1.45 years (IQR, 1.00-3.00) in the control group. The main presenting symptoms were fever (92%), dry cough (74%), and dyspnea (72%). The lymphocytic count was normal in 14 patients (28%), decreased in 16 patients (32%), and increased in 20 patients (40%) of the case group. Positive chest computed tomography finding of COVID-19 infection were demonstrated among 40% of patients using COVID-19 Reporting and Data System categories (ground-glass opacity with or without consolidations in the lungs). There were significant increased serum TACE and TNF-α with decreased ACE2 levels among cases versus controls (P< 0.001). The GG genotype and G allele of the TNF-α-308G/A SNP were significantly higher in patients than in controls (P<0.05 for both), with insignificant differences in genotype and allelic frequencies in the ACE (I/D) (rs4646994) and ACE2 rs2285666 SNPs.

CONCLUSION

The TNF signaling pathway was significantly activated in pediatric COVID-19 infection. Only the TNF-α-308G/A SNP was significantly associated with pediatric COVID-19 infection.

Bariatric surgery and COVID-19 outcomes: results from the PaTH to Health: Diabetes study

BACKGROUND

Obesity and type 2 diabetes mellitus (T2DM) are risk factors for severe COVID-19 infection. Bariatric surgery (BSG) is an effective treatment of obesity through weight loss and may reduce COVID-19 severity.

OBJECTIVES

We examined the effect of BSG on COVID-19 outcomes in patients with or at risk of T2DM.

SETTING

Electronic health record data from the PaTH Clinical Data Research Network, a partnership of 5 health systems reviewed from March 1, 2020, to December 31, 2020.

METHODS

Ambulatory and in-hospital patient encounters with COVID-19 diagnosis and obesity were identified. We constructed 2 patient groups: BSG and non-BSG (NBSG). The BSG group included patients with at least 1 encounter for the BSG procedure code and/or 1 BSG diagnosis code; the NBSG group included patients with no procedure or diagnosis code for BSG with body mass index (BMI) ≥40 or BMI ≥35 and at least 2 obesity-related co-morbidities. We matched 1 patient in the BSG group to 2 patients in the NBSG group based on age, gender (sex defined at birth), race and ethnicity, group (T2DM and at risk of T2DM), and site. The primary outcome was 30-day outcomes of COVID-19 severity.

RESULTS

After matching, we found that patients with BSG had lower odds of respiratory failure (41%) and ventilation/intensive care unit (ICU) admission/death (52%). Patients in the BSG group had lower odds of hospitalization, pneumonia, respiratory failure, and the most severe COVID-19 outcomes combined (ventilation/ICU admission/death). T2DM was identified as a risk factor for COVID-19 severity in the BSG group.

CONCLUSIONS

This retrospective, matched-cohort analysis found BSG to have a protective effect against severe COVID-19 outcomes.

Longitudinal transcriptional changes reveal genes from the natural killer cell-mediated cytotoxicity pathway as critical players underlying COVID-19 progression

Patients present a wide range of clinical severities in response severe acute respiratory syndrome coronavirus 2 infection, but the underlying molecular and cellular reasons why clinical outcomes vary so greatly within the population remains unknown. Here, we report that negative clinical outcomes in severely ill patients were associated with divergent RNA transcriptome profiles in peripheral immune cells compared with mild cases during the first weeks after disease onset. Protein-protein interaction analysis indicated that early-responding cytotoxic natural killer cells were associated with an effective clearance of the virus and a less severe outcome. This innate immune response was associated with the activation of select cytokine-cytokine receptor pathways and robust Th1/Th2 cell differentiation profiles. In contrast, severely ill patients exhibited a dysregulation between innate and adaptive responses affiliated with divergent Th1/Th2 profiles and negative outcomes. This knowledge forms the basis of clinical triage that may be used to preemptively detect high-risk patients before life-threatening outcomes ensue.

Real time monitoring of respiratory viral infections in cohort studies using a smartphone app

Cohort studies investigating respiratory disease pathogenesis aim to pair mechanistic investigations with longitudinal virus detection but are limited by the burden of methods tracking illness over time. In this study, we explored the utility of a purpose-built AERIAL TempTracker smartphone app to assess real-time data collection and adherence monitoring and overall burden to participants, while identifying symptomatic respiratory illnesses in two birth cohort studies. We observed strong adherence with daily app usage over the six-month study period, with positive feedback from participant families. A total of 648 symptomatic respiratory illness events were identified with significant variability between individuals in the frequency, duration, and virus detected. Collectively, our data show that a smartphone app provides a reliable method to capture the longitudinal virus data in cohort studies which facilitates the understanding of early life infections in chronic respiratory disease development.

Severity Profile of COVID-19 in Hospitalized Pediatric Patients

OBJECTIVE

We aimed to describe the clinical characteristics associated with severity in children hospitalized with COVID-19.

METHOD

This was an epidemiological cohort study conducted in two hospitals, one of which was a reference center for the treatment of COVID-19 cases. Data were collected from the reports generated by the hospital epidemiology centers and the medical records of patients aged between 0 and 14 years with a diagnosis of COVID-19, hospitalized between March 2020 and June 2021. To analyze the association between the clinical profile and severity, the cases were classified as severe (severe and critical) and non-severe (asymptomatic, mild, and moderate).

RESULTS

Of the 191 children followed up in the cohort, 73.3% developed the severe form. The percentage of children with oxygen saturation below 95% was 46.6%. In the multivariate analysis, a higher risk of severity was estimated among children with uncontrolled asthma (RR = 13.2), who were overweight or obese (RR = 3.21), who had cough symptoms (RR = 2.72), and those aged under one year (RR = 3.23).

CONCLUSIONS

This result underscores the need to improve healthcare at every level for children and for the management of asthma and nutrition when considering children with this clinical profile who are diagnosed with COVID-19.

Endogenous retroviruses in multiple sclerosis: A network-based etiopathogenic model

The present perspective article proposes an etiopathological model for multiple sclerosis pathogenesis and progression associated with the activation of human endogenous retroviruses. We reviewed preclinical, clinical, epidemiological, and evolutionary evidence indicating how the complex, multi-level interplay of genetic traits and environmental factors contributes to multiple sclerosis. We propose that endogenous retroviruses transactivation acts as a critical node in disease development. We also discuss the rationale for combined anti-retroviral therapy in multiple sclerosis as a disease-modifying therapeutic strategy. Finally, we propose that the immuno-pathogenic process triggered by endogenous retrovirus activation can be extended to aging and aging-related neurodegeneration. In this regard, endogenous retroviruses can be envisioned to act as epigenetic noise, favoring the proliferation of disorganized cellular subpopulations and accelerating system-specific "aging". Since inflammation and aging are two sides of the same coin (plastic dis-adaptation to external stimuli with system-specific degree of freedom), the two conditions may be epiphenomenal products of increased epigenomic entropy. Inflammation accelerates organ-specific aging, disrupting communication throughout critical systems of the body and producing symptoms. Overlapping neurological symptoms and syndromes may emerge from the activity of shared molecular networks that respond to endogenous retroviruses' reactivation.

Genetic variability in the glucocorticoid pathway and treatment outcomes in hospitalized patients with COVID-19: a pilot study

Introduction: Corticosteroids are widely used for the treatment of coronavirus disease (COVID)-19. Genetic polymorphisms of the glucocorticoid receptor, metabolizing enzymes, or transporters may affect treatment response to dexamethasone. This study aimed to evaluate the association of the glucocorticoid pathway polymorphisms with the treatment response and short-term outcomes in patients with severe COVID-19. Methods: Our pilot study included 107 hospitalized patients with COVID-19 treated with dexamethasone and/or methylprednisolone, genotyped for 14 polymorphisms in the glucocorticoid pathway. Results: In total, 83% of patients had severe disease, 15.1% had critical disease and only 1.9% had moderate disease. CYP3A4 rs35599367 was the major genetic determinant of COVID-19 severity as carriers of this polymorphism had higher risk of critical disease (OR = 6.538; 95% confidence interval = 1.19-35.914: p = 0.031) and needed intensive care unit treatment more frequently (OR = 10; 95% CI = 1.754-57.021: p = 0.01). This polymorphism was also associated with worse disease outcomes, as those patients had to switch from dexamethasone to methylprednisolone more often (OR = 6.609; 95% CI = 1.137-38.424: p = 0.036), had longer hospitalization (p = 0.022) and needed longer oxygen supplementation (p = 0.040). Carriers of NR3C1 rs6198 polymorphic allele required shorter dexamethasone treatment (p = 0.043), but had higher odds for switching therapy with methylprednisolone (OR = 2.711; 95% CI = 1.018-7.22: p = 0.046). Furthermore, rs6198 was also associated with longer duration of hospitalization (p = 0.001) and longer oxygen supplementation (p = 0.001). NR3C1 rs33388 polymorphic allele was associated with shorter hospitalization (p = 0.025) and lower odds for ICU treatment (OR = 0.144; 95% CI = 0.027-0.769: p = 0.023). GSTP1 rs1695 was associated with duration of hospitalization (p = 0.015), oxygen supplementation and (p = 0.047) dexamethasone treatment (p = 0.022). Conclusion: Our pathway-based approach enabled us to identify novel candidate polymorphisms that can be used as predictive biomarkers associated with response to glucocorticoid treatment in COVID-19. This could contribute to the patient's stratification and personalized treatment approach.

Gene variants rs5182, rs2074192, and rs4343 in the renin-angiotensin-aldosterone system are associated with symptom severity, higher odds of hospitalization, and death in COVID-19

OBJECTIVES

To analyze the gene variants of the renin-angiotensin-aldosterone system and determine their association with the severity and outcome of COVID-19.

METHODS

A total of 104 patients were included in the study: 34 asymptomatic patients with COVID-19 as controls and 70 symptomatic patients as cases. The genetic variants ACE rs4343, ACE2 rs2074192, AGTR1 rs5182, and AGT rs4762 were identified using TaqMan genotyping tests.

RESULTS

Patients with the T/T genotype of AGTR1 rs5182 have a higher probability of developing symptomatic COVID-19 (odds ratio [OR] 12.25, 95% confidence interval [CI] 1.34-111.9, P ≤0.001) and a higher risk of hospitalization because of disease (OR 14.00, 95% CI 1.53-128.49, P = 0.012). The haplotype CTG (AGTR1 rs5182, ACE2 rs2074192, ACE rs4343) decreased the odds of death related to COVID-19 in the study population (OR 0.03, 95% CI 0.0-0.06, P = 0.026).

CONCLUSIONS

The T/T genotype of the AGTR1 rs5182 variant increased the probability of symptomatic COVID-19 and hospitalization, whereas the haplotype CTG (consisting of AGTR1 rs5182, ACE2 rs2074192, and ACE rs4343) decreased the odds of death related to COVID-19 by 97% in the hospitalized patients with COVID-19. These results support the participation of renin-angiotensin-aldosterone system gene variants as modifiers of the severity of symptoms associated with SARS-CoV-2 infection and the outcome of COVID-19.

The effects of killer cell immunoglobulin-like receptor (KIR) genes on susceptibility to severe COVID-19 in the Iranian population

BACKGROUND

Variations in the innate and adaptive immune response systems are linked to variations in the severity of COVID-19. Natural killer cell (NK) function is regulated by sophisticated receptor system including Killer-cell immunoglobulin-like receptor (KIR) family. We aimed to investigate the impact of possessing certain KIR genes and genotypes on COVID19 severity in Iranians. KIR genotyping was performed on 394 age/sex matched Iranians with no underlying conditions who developed mild and severe COVID- 19. The presence and/or absence of 11 KIR genes were determined using the PCR with sequence specific primers (PCR-SSP).

RESULTS

Patients with mild symptoms had higher frequency ofKIR2DS1 (p = 0.004) and KIR2DS2 (p = 0.017) genes compared to those with severe disease. While KIR3DL3 and deleted variant of KIR2DS4 occurred more frequently in patients who developed a severe form of the disease. In this study, a significant increase of and B haplotype was observed in the Mild group compared to the Severe group (respectively, p = 0.002 and p = 0.02). Also, the prevalence of haplotype A was significantly higher in the Severe group than in the Mild group (p = 0.02).

CONCLUSIONS

These results suggest that the KIR2DS1, KIR2DS, and B haplotype maybe have a protective effect against COVID-19 severity. The results also suggest the inhibitory gene KIR2DL3 and haplotype A are risk factors for the severity of COVID-19.

The Evaluation of Drugs as Potential Modulators of the Trafficking and Maturation of ACE2, the SARS-CoV-2 Receptor

ACE2, part of the angiotensin-converting enzyme family and the renin-angiotensin-aldosterone system (RAAS), plays vital roles in cardiovascular and renal functions. It is also the primary receptor for SARS-CoV-2, enabling its entry into cells. This project aimed to study ACE2's cellular trafficking and maturation to the cell surface and assess the impact of various drugs and compounds on these processes. We used cellular and biochemical analyses to evaluate these compounds as potential leads for COVID-19 therapeutics. Our screening assay focused on ACE2 maturation levels and subcellular localization with and without drug treatments. Results showed that ACE2 maturation is generally fast and robust, with certain drugs having a mild impact. Out of twenty-three tested compounds, eight significantly reduced ACE2 maturation levels, and three caused approximately 20% decreases. Screening trafficking inhibitors revealed significant effects from most molecular modulators of protein trafficking, mild effects from most proposed COVID-19 drugs, and no effects from statins. This study noted that manipulating ACE2 levels could be beneficial or harmful, depending on the context. Thus, using this approach to uncover leads for COVID-19 therapeutics requires a thorough understanding ACE2's biogenesis and biology.

Association of specific ACE2 and TMPRSS2 variants with circulatory cytokines of COVID-19 Emirati patients

INTRODUCTION

The COVID-19 pandemic represented one of the most significant challenges to researchers and healthcare providers. Several factors determine the disease severity, whereas none alone can explain the tremendous variability. The Single nucleotide variants (SNVs) in angiotensin-converting enzyme-2 (ACE2) and transmembrane serine protease type-2 (TMPRSS2) genes affect the virus entry and are considered possible risk factors for COVID-19.

METHODS

We compiled a panel of gene variants from both genes and used in-silico analysis to predict their significance. We performed biological validation to assess their capacity to alter the ACE2 interaction with the virus spike protein. Subsequently, we conducted a retrospective comparative genome analysis on those variants in the Emirati patients with different disease severity (total of 96) along with 69 healthy control subjects.

RESULTS

Our results showed that the Emirati population lacks the variants that were previously reported as associated with disease severity, whereas a new variant in ACE2 "Chr X:g.15584534" was associated with disease severity specifically among female patients. In-silico analysis revealed that the new variant can determine the ACE2 gene transcription. Several cytokines (GM-CSF and IL-6) and chemokines (MCP-1/CCL2, IL-8/CXCL8, and IP-10/CXCL10) were markedly increased in COVID-19 patients with a significant correlation with disease severity. The newly reported genetic variant of ACE2 showed a positive correlation with CD40L, IL-1β, IL-2, IL-15, and IL-17A in COVID-19 patients.

CONCLUSION

Whereas COVID-19 represents now a past pandemic, our study underscores the importance of genetic factors specific to a population, which can influence both the susceptibility to viral infections and the level of severity; subsequently expected required preparedness in different areas of the world.

Relationship of Toll-Like Receptor 7, 9, and 10 Polymorphisms and the Severity of Coronavirus Disease 2019

Coronavirus disease 2019 (COVID-19) is a pandemic that is still affecting people and has caused many deaths. Toll-like receptors (TLRs) have an important role in the binding of disease agents to the host cell, disease susceptibility and severity, and host disease resistance. In this study, we investigated the frequencies of TLR7 (C.4-151 A/G), TLR9 (T-1486C and G2848A), and TLR10 (720A/C and 992T/A) single nucleotide polymorphisms in 150 cases with COVID-19 and 171 control samples. We also examined whether TLR7, TLR9, and TLR10 were related to COVID-19 severity. Furthermore, we analyzed the association between COVID-19 and some clinical parameters. Polymerase chain reaction based on restriction fragment length polymorphisms performed for the TLR7, TLR9, and TLR10 single nucleotide polymorphisms. TLR7 C.4-151 A/G G allele and GG genotype; TLR9 T-1486C C allele and TC, CC genotypes; and TLR10 720A/C C allele; TLR10 992T/A A allele and AA genotype frequencies were statistically significant in cases with COVID-19 compared with controls (P < 0.05*). In addition, there was a statistically significant difference in the distribution of TLR7, TLR9, and TLR10 allele and genotype frequencies between the severity groups (P < 0.05*). Our findings suggest that TLR7, TLR9, and TLR10 polymorphisms may be crucial for the clinical course and susceptibility to infection.

Exposure to urban particulate matter alters responses of olfactory mucosal cells to SARS-CoV-2 infection

Respiratory viruses have a significant impact on health, as highlighted by the COVID-19 pandemic. Exposure to air pollution can contribute to viral susceptibility and be associated with severe outcomes, as suggested by recent epidemiological studies. Furthermore, exposure to particulate matter (PM), an important constituent of air pollution, is linked to adverse effects on the brain, including cognitive decline and Alzheimer's disease (AD). The olfactory mucosa (OM), a tissue located at the rooftop of the nasal cavity, is directly exposed to inhaled air and in direct contact with the brain. Increasing evidence of OM dysfunction related to neuropathogenesis and viral infection demonstrates the importance of elucidating the interplay between viruses and air pollutants at the OM. This study examined the effects of subacute exposure to urban PM 0.2 and PM 10-2.5 on SARS-CoV-2 infection using primary human OM cells obtained from cognitively healthy individuals and individuals diagnosed with AD. OM cells were exposed to PM and subsequently infected with the SARS-CoV-2 virus in the presence of pollutants. SARS-CoV-2 entry receptors and replication, toxicological endpoints, cytokine release, oxidative stress markers, and amyloid beta levels were measured. Exposure to PM did not enhance the expression of viral entry receptors or cellular viral load in human OM cells. However, PM-exposed and SARS-CoV-2-infected cells showed alterations in cellular and immune responses when compared to cells infected only with the virus or pollutants. These changes are highly pronounced in AD OM cells. These results suggest that exposure of human OM cells to PM does not increase susceptibility to SARS-CoV-2 infection in vitro, but it can alter cellular immune responses to the virus, particularly in AD. Understanding the interplay of air pollutants and COVID-19 can provide important insight for the development of public health policies and interventions to reduce the negative influences of air pollution exposure.

Molecular evolutionary characteristics of severe acute respiratory syndrome coronavirus 2 and the relatedness of epidemiological and socio-environmental factors

After the first outbreak, SARS-CoV-2 infection continues to occur due to the emergence of new variants. There is limited information available on the comparative evaluation of evolutionary characteristics of SARS-CoV-2 among different countries over time, and its relatedness to epidemiological and socio-environmental factors within those countries. We assessed comparative Bayesian evolutionary characteristics for SARS-CoV-2 in eight countries from 2020 to 2022 using BEAST version 2.6.7. Additionally, the relatedness between virus evolution factors and both epidemiological and socio-environmental factors was analyzed using Pearson's correlation coefficient. The estimated substitution rates in the gene encoding S protein of SARS-CoV-2 exhibited a continuous increase from 2020 to 2022 and were divided into two distinct groups in 2022 (p value < 0.05). Effective population size (Ne) generally showed decreased patterns by time. Notably, the change rates of the substitution rates were negatively correlated with the cumulative vaccination rates in 2021. A strict and rapid vaccination policy in the United Arab Emirates dramatically reduced the evolution of the virus, compared to other countries. Also, the average yearly temperature in countries were negatively correlated with the substitution rates. The changes of six epitopes in SARS-CoV-2 were related to various socio-environmental factors. We figured out comparative virus evolutionary traits and the association of epidemiological and socio-environmental factors especially cumulative vaccination rates and average temperature.

Relationship between ultra-short heart rate variability and short-term mortality in hospitalized COVID-19 patients

OBJECTIVE

To assess the association between ultra-short heart rate variability (US-HRV) and short-term mortality in patients with COVID-19 and develop prognostic prediction models to identify high-risk patients as early as possible.

METHODS

A retrospective cohort study was performed on 488 patients diagnosed with COVID-19 and hospitalized in the First Affiliated Hospital of Fujian Medical University from December 2022 to January 2023. 10-s electrocardiogram (ECG) data were available for these patients. The US-HRV parameters including standard deviation of all normal-to-normal R-R intervals (SDNN) and root mean square of successive differences between normal-to-normal R-R intervals (rMSSD) were calculated using Nalong ECG software. The endpoint was short-term mortality, including in-hospital mortality or mortality within 1 week after discharge.

RESULTS

Of the 488 patients, 76 (15.6%) died. The SDNN and rMSSD in the death group were significantly lower than those in the survival group (P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for SDNN and rMSSD to predict mortality was 0.761 and 0.715, respectively. The combined use of SDNN and rMSSD had an AUC of 0.774. The mortality rate in the group with SDNN ≤7.5 ms was higher than that of SDNN >7.5 ms group (P < 0.05). With the decrease of SDNN, the mortality of patients showed an upward trend, and the mortality of patients with SDNN ≤2 ms was the highest (66.7%). Multivariate logistic regression analysis identified SDNN as an independent predictor of prognosis (odds ratio (OR) = 5.791, 95% confidential interval (CI) 1.615-20.765, P = 0.007). The AUC of Model 1 (simple model) was 0.866 (95% CI 0.826-0.905). The AUC of Model 2 (comprehensive model) was 0.914 (95% CI 0.881-0.947).

CONCLUSION

SDNN was associated with short-term mortality and provided the additional discriminatory power of the risk stratification model for hospitalized COVID-19 patients.

SARS-CoV-2 Infection-Dependent Modulation in Vital Components of the Serum Profile of Severely SARS-CoV-2 Infected Patients

Background

COVID-19 modulates many serological biomarkers during the progress of disease severity. The study aimed to determine COVID-19 severity-associated perturbance in the serum profile.

Methods

A retrospective study including COVID-19-positive individuals (n = 405) was accomplished. The serum profile of COVID-19 participants was mined from laboratory records. Severity-associated alteration in the serum profile was evaluated using Pearson correlation, regression, VCramer, Bayesian posterior VCramer, and bias factor using R-base-RStudio-version-3.3.0 with a significant cut-off of p < 0.05.

Results

Significantly different mean ± standard deviation (SD) (highly versus moderately severe) of C-reactive protein (CRP), ferritin, neutrophil-lymphocyte ratio (NLR), D-dimer, platelets, prothrombin time (PT), partial prothrombin time (PTT), troponin 1, lactate dehydrogenase (LDH), aspartate-aminotransferase (AST), alanine aminotransferase (ALT), and AST/ALT ratio was observed (p < 0.001). Highly severe COVID-19 associated with CRP, ferritin, NLR, in D-dimer, PT, PTT, troponin 1, AST/ALT ratio, AST and ALT (adjusted odds ratio (AOR): 1.346, 1.05, 1.46, 1.33, 1.42, 1.23, 4.07, 3.9, 1.24, 1.45, p < 0.001). CRP with ferritin (r = 0.743), NLR (r = 0.77), white blood cells (WBC) (r = 0.8), troponin1 with LDH (r = 0.757), and D-dimer with platelets (r = -0.81) were highly correlated. X2pearson (p < 0.001), VCramer (0.71), Bayesian-VCramer (0.7), and bias-factor (-125) for troponin 1 indicate the strong association of troponin 1 level and with COVID-19 severity. X2pearson (p < 0.001), VCramer (1), Bayesian-VCramer (0.98), and bias-factor (-266.3) for NLR exhibited a very strong association of pathologic conditions with the high severity of the disease.

Conclusion

These biomarkers of inflammation (CRP, Ferritin, NLR), coagulation disorders (D-dimer, PT, and PTT) cardiac abnormality (troponin 1), and liver injury (AST/ALT) could be crucial in low-medical resource settings as potential prognosticator/predictors of the COVID-19 severity and clinical outcomes. Moreover, the outcome of this study could be leveraged for the early prediction of disease severity during SARS-CoV or Middle East Respiratory Coronavirus (MERS-CoV) infection.

Graphene transistor-based biosensors for rapid detection of SARS-CoV-2

Field-effect transistor (FET) biosensors use FETs to detect changes in the amount of electrical charge caused by biomolecules like antigens and antibodies. COVID-19 can be detected by employing these biosensors by immobilising bio-receptor molecules that bind to the SARS-CoV-2 virus on the FET channel surface and subsequent monitoring of the changes in the current triggered by the virus. Graphene Field-effect Transistor (GFET)-based biosensors utilise graphene, a two-dimensional material with high electrical conductivity, as the sensing element. These biosensors can rapidly detect several biomolecules including the SARS-CoV-2 virus, which is responsible for COVID-19. GFETs are ideal for real-time infectious illness diagnosis due to their great sensitivity and specificity. These graphene transistor-based biosensors could revolutionise clinical diagnostics by generating fast, accurate data that could aid pandemic management. GFETs can also be integrated into point-of-care (POC) diagnostic equipment. Recent advances in GFET-type biosensors for SARS-CoV-2 detection are discussed here, along with their associated challenges and future scope.

Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia

BACKGROUND

Identifying prognosticators/predictors of COVID-19 severity is the principal focus for early prediction and effective management of the disease in a time-bound and cost-effective manner. We aimed to evaluate COVID-19 severity-dependent alteration in inflammatory and coagulopathy biomarkers.

METHODS

A hospital-dependent retrospective observational study (total: n = 377; male, n = 213; and female, n = 164 participants) was undertaken. COVID-19 exposure was assessed by performing real-time PCR on nasopharyngeal (NP) swabs. Descriptive and inferential statistics were applied for both continuous and categorical variables using Rstudio-version-4.0.2. Pearson correlation and regression were executed with a cut-off of p < 0.05 for evaluating significance. Data representation by R-packages and ggplot2.

RESULTS

A significant variation in the mean ± SD (highly-sever (HS)/moderately severe (MS)) of CRP (HS/MS: 102.4 ± 22.9/21.3 ± 6.9, p-value < 0.001), D-dimer (HS/MS: 661.1 ± 80.6/348.7 ± 42.9, p-value < 0.001), and ferritin (HS/MS: 875.8 ± 126.8/593.4 ± 67.3, p-value < 0.001) were observed. Thrombocytopenia, high PT, and PTT exhibited an association with the HS individuals (p < 0.001). CRP was correlated with neutrophil (r = 0.77), ferritin (r = 0.74), and WBC (r = 0.8). D-dimer correlated with platelets (r = -0.82), PT (r = 0.22), and PTT (r = 0.37). The adjusted odds ratios (Ad-OR) of CRP, ferritin, D-dimer, platelet, PT, and PTT for HS compared to MS were 1.30 (95% CI -1.137, 1.50; p < 0.001), 1.048 (95% CI -1.03, 1.066; p < 0.001), 1.3 (95% CI -1.24, 1.49, p > 0.05), -0.813 (95% CI -0.734, 0.899, p < 0.001), 1.347 (95% CI -1.15, 1.57, p < 0.001), and 1.234 (95% CI -1.16, 1.314, p < 0.001), respectively.

CONCLUSION

SARS-CoV-2 caused alterations in vital laboratory parameters and raised ferritin, CRP, and D-dimer presented an association with disease severity at a significant level.

Evaluating COVID-19 Risk to Essential Workers by Occupational Group: A Case Study in Massachusetts

Occupational exposure to SARS-CoV-2 varies by profession, but "essential workers" are often considered in aggregate in COVID-19 models. This aggregation complicates efforts to understand risks to specific types of workers or industries and target interventions, specifically towards non-healthcare workers. We used census tract-resolution American Community Survey data to develop novel essential worker categories among the occupations designated as COVID-19 Essential Services in Massachusetts. Census tract-resolution COVID-19 cases and deaths were provided by the Massachusetts Department of Public Health. We evaluated the association between essential worker categories and cases and deaths over two phases of the pandemic from March 2020 to February 2021 using adjusted mixed-effects negative binomial regression, controlling for other sociodemographic risk factors. We observed elevated COVID-19 case incidence in census tracts in the highest tertile of workers in construction/transportation/buildings maintenance (Phase 1: IRR 1.32 [95% CI 1.22, 1.42]; Phase 2: IRR: 1.19 [1.13, 1.25]), production (Phase 1: IRR: 1.23 [1.15, 1.33]; Phase 2: 1.18 [1.12, 1.24]), and public-facing sales and services occupations (Phase 1: IRR: 1.14 [1.07, 1.21]; Phase 2: IRR: 1.10 [1.06, 1.15]). We found reduced case incidence associated with greater percentage of essential workers able to work from home (Phase 1: IRR: 0.85 [0.78, 0.94]; Phase 2: IRR: 0.83 [0.77, 0.88]). Similar trends exist in the associations between essential worker categories and deaths, though attenuated. Estimating industry-specific risk for essential workers is important in targeting interventions for COVID-19 and other diseases and our categories provide a reproducible and straightforward way to support such efforts.

Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) +49A>G (rs231775) gene polymorphism is not associated with COVID-19 severity and mortality in an Iranian population

Introduction

Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) regulates T cell immune responses as an immune activation inhibitor. Literature reviews suggest that COVID-19 is associated with dysregulation of the inflammatory immune response. The purpose of the present hospital-based case-control study was to evaluate the genetic association of the CTLA4 +49A > G (rs231775) Single Nucleotide Polymorphism (SNP) with COVID-19 severity and mortality among the Iranian people.

Method

Genomic DNA of peripheral blood nuclear cells was extracted from the 794 COVID-19 patients and 167 control individuals. The polymorphic site of rs231775 was genotyped using the PCR-RFLP technique. Also, to identify whether this genetic variation was related to CTLA-4 mRNA expression, total RNA was extracted from 178 COVID-19 patients and 70 controls. The mRNA levels of CTLA-4 were determined using real-time PCR.

Result

There were no statistically significant differences found in the genotype and allele frequencies among the different genetic models with regards to the severity and mortality of COVID-19. Furthermore, there was no significant association between rs231775 genotypes and CTLA-4 mRNA expression in patients.

Conclusion

Our findings demonstrated that SARS-CoV-2 infection is not associated with rs231775 in the Iranian people. More investigations are crucial to show how this genetic variation affects other ethnic groups. Given the importance of CTLA-4 in regulating immune responses, further studies are recommended to examine other CTLA-4 SNPs and the function of this gene in COVID-19 patients.

Hematological and Biochemical Laboratory Parameters in COVID-19 Patients: A Retrospective Modeling Study of Severity and Mortality Predictors

Background

It is well known that laboratory markers could help in identifying risk factors of severe illness and predicting outcomes of diseases. Here, we performed a retrospective modeling study of severity and mortality predictors of hematological and biochemical laboratory parameters in Iranian COVID-19 patients.

Methods

Data were obtained retrospectively from medical records of 564 confirmed Iranian COVID-19 cases. According to the disease severity, the patients were categorized into two groups (severe or nonsevere), and based on the outcome of the disease, patients were divided into two groups (recovered or deceased). Demographic and laboratory data were compared between groups, and statistical analyses were performed to define predictors of disease severity and mortality in the patients.

Results

The study identified a panel of hematological and biochemical markers associated with the severe outcome of COVID-19 and constructed different predictive models for severity and mortality. The disease severity and mortality rate were significantly higher in elderly inpatients, whereas gender was not a determining factor of the clinical outcome. Age-adjusted white blood cells (WBC), platelet cells (PLT), neutrophil-to-lymphocyte ratio (NLR), red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), erythrocyte sedimentation rate (ESR), mean corpuscular hemoglobin (MCHC), blood urea nitrogen (BUN), and creatinine (Cr) also showed high accuracy in predicting severe cases at the time of hospitalization, and logistic regression analysis suggested grouped hematological parameters (age, WBC, NLR, PLT, HGB, and international normalized ratio (INR)) and biochemical markers (age, BUN, and lactate dehydrogenase (LDH)) as the best models of combined laboratory predictors for severity and mortality.

Conclusion

The findings suggest that a panel of several routine laboratory parameters recorded on admission could be helpful for clinicians to predict and evaluate the risk of disease severity and mortality in COVID-19 patients.

[Association of polymorphic variants of hemostatic system genes with the course of COVID-19]

INTRODUCTION

COVID-19 is characterized by a varied clinical course. The aim of the work was to identify associations of SNPs of hemostatic system genes with COVID-19.

MATERIALS AND METHODS

DNA was isolated from patients (n=117) and healthy participants (n=104). All infected patients were divided into 3 groups, depending on disease severity assessment, which was appreciated by NEWS2. Another group consisted of participants, who had asymptomatic infection in the past. Determination of SNPs of the genes FGB (-455 G/A), FII (20210 G/A), FV (1691 G/A), FVII (10976 G/A), FXIIIA1 (103 G/T), ITGA2 (807 C/T), ITGB3 (1565 T/C), SERPINE1 (-675 5G/4G) were performed by PCR using the "Genetics of Hemostasis" kit ("DNA-Technology", Russia).

RESULTS

In analyzed SNPs, no significant differences were detected between the group of infected patients and healthy participants. But significant association was revealed in gene SERPINE1 (-675 5G/4G), when patient groups, differing in the disease severity, were analyzed relative to the group of participants with asymptomatic infection (p=0.0381; p=0 .0066; p=0.0009). It was found, that as COVID-19 severity scores increased, the proportion of 5G allele of gene SERPINE1 decreased, and the proportion of the 4G allele increased (p=0.005; p=0.009; p=0.0005). Similar processes were observed for genotypes 5G/5G and 4G/4G.

DISCUSSION

The gene SERPINE1 (-675 5G/4G) is associated with the severity of COVID-19.

CONCLUSION

For the first time, it was discovered that 5G/5G genotype of gene SERPINE1 (-675 5G/4G) can be a marker of a milder course of COVID-19, and the 4G/4G genotype as a more severe one.

Clinical and laboratory characteristics of children with severe and nonsevere COVID-19 in Kermanshah, west of Iran: A retrospective study

Background and Aims

The study aimed to collect and compare clinical and laboratory findings of children with severe and nonsevere COVID-19 in Kermanshah City, located in the west of Iran.

Methods

The study was conducted on 500 children with COVID-19 hospitalized in Mohammad-Kermanshahi Hospital in Kermanshah City. Pediatric COVID-19 was confirmed by reverse transcription-polymerase chain reaction (RT-PCR) test using respiratory secretion samples. Medical records were reviewed and information related to demographic characteristics, underlying diseases, clinical manifestations, laboratory findings, and chest computed tomography (CT) scans were all extracted from electronic and paper records. Patients were divided into three groups according to the severity of the disease: mild, moderate, and severe. Clinical and laboratory findings were compared between the groups and the collected data were analyzed by statistical methods.

Results

Out of 500 patients, 286 were boys and 214 were girls. Of the patients, 321 cases were only COVID-19, while 179 patients were diagnosed as Multisystem Inflammatory Syndrome in Children (MIS-C) positive. The average age of COVID-19 patients was 3.85 ± 4.48 and of MIS-C patients was 3.1 ± 3.5. In order, fever, cough, and heart disorders were the most common symptoms in patients with COVID-19 and MIS-C, respectively. In terms of disease severity, 246 patients had mild disease, 19 patients had moderate disease, and 56 patients had severe disease. In severe patients, the average number of white blood cells (WBC) was higher, while the average number of lymphocytes was lower. Also, in these patients, the average age was lower, and most of them had respiratory distress. In mild patients, often cough, diarrhea, and vomiting were observed.

Conclusion

The results of our study showed that laboratory factors such as WBC count, lymphocyte count, CT findings, Respiratory distress, cough, diarrhea, and vomiting can be used to evaluate the severity of COVID-19 in children.

Virulence traits and novel drug delivery strategies for mucormycosis post-COVID-19: a comprehensive review

The outbreak of a fatal black fungus infection after the resurgence of the cadaverous COVID-19 has exhorted scientists worldwide to develop a nutshell by repurposing or designing new formulations to address the crisis. Patients expressing COVID-19 are more susceptible to Mucormycosis (MCR) and thus fall easy prey to decease accounting for this global threat. Their mortality rates range around 32-70% depending on the organs affected and grow even higher despite the treatment. The many contemporary recommendations strongly advise using liposomal amphotericin B and surgery as first-line therapy whenever practicable. MCR is a dangerous infection that requires an antifungal drug administration on appropriate prescription, typically one of the following: Amphotericin B, Posaconazole, or Isavuconazole since the fungi that cause MCR are resistant to other medications like fluconazole, voriconazole, and echinocandins. Amphotericin B and Posaconazole are administered through veins (intravenously), and isavuconazole by mouth (orally). From last several years so many compounds are developed against invasive fungal disease but only few of them are able to induce effective treatment against the micorals. Adjuvant medicines, more particularly, are difficult to assess without prospective randomized controlled investigations, which are challenging to conduct given the lower incidence and higher mortality from Mucormycosis. The present analysis provides insight into pathogenesis, epidemiology, clinical manifestations, underlying fungal virulence, and growth mechanisms. In addition, current therapy for MCR in Post Covid-19 individuals includes conventional and novel nano-based advanced management systems for procuring against deadly fungal infection. The study urges involving nanomedicine to prevent fungal growth at the commencement of infection, delay the progression, and mitigate fatality risk.

The Dark Side of Nosocomial Infections in Critically Ill COVID-19 Patients

Coronavirus disease 2019 (COVID-19) is a potentially serious acute respiratory infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Since the World Health Organization (WHO) declared COVID-19 a global pandemic, the virus has spread to more than 200 countries with more than 500 million cases and more than 6 million deaths reported globally. It has long been known that viral respiratory tract infections predispose patients to bacterial infections and that these co-infections often have an unfavourable clinical outcome. Moreover, nosocomial infections, also known as healthcare-associated infections (HAIs), are those infections that are absent at the time of admission and acquired after hospitalization. However, the impact of coinfections or secondary infections on the progression of COVID-19 disease and its lethal outcome is still debated. The aim of this review was to assess the literature on the incidence of bacterial co-infections and superinfections in patients with COVID-19. The review also highlights the importance of the rational use of antibiotics in patients with COVID-19 and the need to implement antimicrobial stewardship principles to prevent the transmission of drug-resistant organisms in healthcare settings. Finally, alternative antimicrobial agents to counter the emergence of multidrug-resistant bacteria causing healthcare-associated infections in COVID-19 patients will also be discussed.

Multiple manifestations of uncontrolled asthma increase the risk of severe COVID-19

OBJECTIVE

Asthma control is of importance when assessing the risk of severe outcomes of COVID-19. The aim of this study was to explore associations of clinical characteristics and the effect of multiple manifestations of uncontrolled asthma with severe COVID-19.

METHODS

In 2014-2020, adult patients with uncontrolled asthma, defined as Asthma Control Test (ACT) ≤19 were identified in the Swedish National Airway Register (SNAR) (n = 24533). The SNAR database, including clinical data, was linked with national registers to identify patients with severe COVID-19 (n = 221). The effect of multiple manifestations of uncontrolled asthma was based on: 1) ACT ≤15, 2) frequent exacerbations and 3) previous asthma inpatient/secondary care and evaluated stepwise. Poisson regression analyses were conducted with severe COVID-19 as the dependent variable.

RESULTS

In this cohort with uncontrolled asthma, obesity was the strongest independent risk factor for severe COVID-19 in both sexes, but even greater in men. Multiple manifestations of uncontrolled asthma were more common among those with severe COVID-19 vs. without: one, 45.7 vs. 42.3%, two, 18.1 vs. 9.1% and three, 5.0 vs. 2.1%. The risk ratio (RR) of severe COVID-19 increased with an increasing number of manifestations of uncontrolled asthma: one, RR 1.49 (95% CI 1.09-2.02), two, RR 2.42 (95% CI 1.64-3.57) and three, RR 2.96 (95% CI 1.57-5.60), when adjusted for sex, age, and BMI.

CONCLUSIONS

It is important to consider the effect of multiple manifestations of uncontrolled asthma and obesity when assessing patients with COVID-19, as this increases the risk of severe outcomes substantially.

Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk.

METHODS

A total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance.

RESULTS

We detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk.

CONCLUSION

In this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.

Lung Inflammation Induced by Inactivated SARS-CoV-2 in C57BL/6 Female Mice Is Controlled by Intranasal Instillation of Vitamin D

The COVID-19 pandemic was triggered by the coronavirus SARS-CoV-2, whose peak occurred in the years 2020 and 2021. The main target of this virus is the lung, and the infection is associated with an accentuated inflammatory process involving mainly the innate arm of the immune system. Here, we described the induction of a pulmonary inflammatory process triggered by the intranasal (IN) instillation of UV-inactivated SARS-CoV-2 in C57BL/6 female mice, and then the evaluation of the ability of vitamin D (VitD) to control this process. The assays used to estimate the severity of lung involvement included the total and differential number of cells in the bronchoalveolar lavage fluid (BALF), histopathological analysis, quantification of T cell subsets, and inflammatory mediators by RT-PCR, cytokine quantification in lung homogenates, and flow cytometric analysis of cells recovered from lung parenchyma. The IN instillation of inactivated SARS-CoV-2 triggered a pulmonary inflammatory process, consisting of various cell types and mediators, resembling the typical inflammation found in transgenic mice infected with SARS-CoV-2. This inflammatory process was significantly decreased by the IN delivery of VitD, but not by its IP administration, suggesting that this hormone could have a therapeutic potential in COVID-19 if locally applied. To our knowledge, the local delivery of VitD to downmodulate lung inflammation in COVID-19 is an original proposition.

Risk factors predicting disease severity and mortality in coronavirus disease 2019 Saudi Arabian patients

CONTEXT

Coronavirus disease 2019 (COVID-19) became a global pandemic that may be associated with significant associated risk factors.

AIMS

The aim of this study was to evaluate the factors predisposing risk to death in COVID-19 patients.

SETTINGS AND DESIGN

This is a retrospective study that presents the demographic, clinical presentation, and laboratory findings on our patients to determine risk factors contributing to their COVID-19 outcome.

METHODS

We used logistic regression (odds ratios) to examine associations between clinical findings and risk of death in COVID-19 patients. All analyses were done using STATA 15.

RESULTS

A total of 206 COVID-19 patients were investigated, 28 of them died, and 178 survived. Expired patients were older (74.04 ± 14.45 vs. 55.56 ± 18.41 in those who survived) and mainly of male gender (75% vs. 42% in those who survived). The following factors were strong predictors of death: hypertension (OR: 5.48, 95% CI: 2.10-13.59, P < 0.001), cardiac disease (OR: 5.08, 95% CI: 1.88-13.74, P = 0.001), and hospital admission (OR: 39.75, 95% CI: 5.28-299.12, P < 0.001). In addition, blood group B was more frequent in expired patients (OR: 2.27, 95% CI: 0.78-5.95, P = 0.065).

CONCLUSIONS

Our work adds to the current knowledge about the factors predisposing to death in COVID-19 patient. In our cohort, expired patients were of older age and male gender plus they were more likely to have hypertension, cardiac disease, and hospital severe disease. These factors might be used to evaluate risk of death in patients recently diagnosed of COVID-19.

The investigation of host genetic variants of toll-like receptor 7 and 8 in COVID-19

Toll-like receptors (TLRs) recognize infectious agents and play an important role in the innate immune system. Studies have suggested that TLR single nucleotide polymorphisms (SNPs) are associated with poor antiviral responses against SARS-CoV-2. Therefore, we aimed to investigate the relationship of TLR7 and TLR8 (SNPs) with COVID-19 disease prognosis. A total of 120 COVID-19 patients, 40 outpatients, 40 clinical ward patients and 40 intensive care unit (ICU) patients were included in the study. TLR7 (rs179009), TLR8-129 C/G (rs3764879) and TLR8 Met1Val (rs3764880) SNPs were genotyped using the PCR-RFLP method. In female patients, individuals carrying AG genotype and G allele for TLR8 Met1Val SNP were found at a higher frequency in patients hospitalized in the ICU than in patients followed in the clinical ward (p < 0.05). In terms of the other two SNPs, no significant difference was found between the groups in females. Furthermore, in male patients, A allele of TLR7 rs179009 SNP was at a higher frequency in patients who have at least one comorbidity than in patients who have no comorbidity (p < 0.05). Our results suggest that TLR8 Met1Val SNP is important in the COVID-19 disease severity in females. Furthermore, TLR7 rs179009 SNP is important in male patients in the presence of comorbid diseases.

Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection leads to a wide range of clinical manifestations and determines the need for personalized and precision medicine. To better understand the biological determinants of this heterogeneity, we explored the plasma proteome of 43 COVID-19 patients with different outcomes by an untargeted liquid chromatography-mass spectrometry approach. The comparison between asymptomatic or pauci-symptomatic subjects (MILDs), and hospitalised patients in need of oxygen support therapy (SEVEREs) highlighted 29 proteins emerged as differentially expressed: 12 overexpressed in MILDs and 17 in SEVEREs. Moreover, a supervised analysis based on a decision-tree recognised three proteins (Fetuin-A, Ig lambda-2chain-C-region, Vitronectin) that are able to robustly discriminate between the two classes independently from the infection stage. In silico functional annotation of the 29 deregulated proteins pinpointed several functions possibly related to the severity; no pathway was associated exclusively to MILDs, while several only to SEVEREs, and some associated to both MILDs and SEVEREs; SARS-CoV-2 signalling pathway was significantly enriched by proteins up-expressed in SEVEREs (SAA1/2, CRP, HP, LRG1) and in MILDs (GSN, HRG). In conclusion, our analysis could provide key information for 'proteomically' defining possible upstream mechanisms and mediators triggering or limiting the domino effect of the immune-related response and characterizing severe exacerbations.

A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection

Background

The variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged repeatedly, especially the Omicron strain which is extremely infectious, so early identification of patients who may develop critical illness will aid in delivering proper treatment and optimizing use of resources. We aimed to develop and validate a practical scoring model at hospital admission for predicting which patients with Omicron infection will develop critical illness.

Methods

A total of 2,459 patients with Omicron infection were enrolled in this retrospective study. Univariate and multivariate logistic regression analysis were performed to evaluate predictors associated with critical illness. Moreover, the area under the receiver operating characteristic curve (AUROC), continuous net reclassification improvement, and integrated discrimination index were assessed.

Results

The derivation cohort included 1721 patients and the validation cohort included 738 patients. A total of 98 patients developed critical illness. Thirteen variables were independent predictive factors and were included in the risk score: age > 65, C-reactive protein > 10 mg/L, lactate dehydrogenase > 250 U/L, lymphocyte < 0.8*10^⁹/L, white blood cell > 10*10^⁹/L, Oxygen saturation < 90%, malignancy, chronic kidney disease, chronic cardiac disease, chronic obstructive pulmonary disease, diabetes, cerebrovascular disease, and non-vaccination. AUROC in the derivation cohort and validation cohort were 0.926 (95% CI, 0.903-0.948) and 0.907 (95% CI, 0.860-0.955), respectively. Moreover, the critical illness risk scoring model had the highest AUROC compared with CURB-65, sequential organ failure assessment (SOFA) and 4C mortality scores, and always obtained more net benefit.

Conclusion

The risk scoring model based on the characteristics of patients at the time of admission to the hospital may help medical practitioners to identify critically ill patients and take prompt measures.

Association between ABO blood groups and the risk of infection with SARS-CoV-2 in Iraq

OBJECTIVE

The primary goals of this research were to analyze the relationship between ABO blood types and the severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and investigate the effect of vaccination in Iraq.

METHODS

Data and outcomes were gathered from the medical records of 200 patients. Patients were categorized by blood group and vaccination status in the analysis.

RESULTS

In total, 200 hospitalized patients (125 men and 75 women) with confirmed SARS-CoV-2 infection and blood group (ABO) and clinical data were enrolled. Of the 200 patients, 155 (77.5%) were vaccinated against SARS-CoV-2. The results illustrated that 25 patients died, which might have been attributable to a lack of vaccination or older age. Our analysis revealed that blood group O individuals were much less likely to be infected by SARS-CoV-2 than non-O subjects, whereas blood group A individuals carried a higher risk of infection.

CONCLUSIONS

Our findings illustrated that immunization significantly reduces COVID-19 risk across all age groups, but there has been an increase in the number of cases because of decreased vaccine efficacy in older patients and persons with comorbidities. However, 45% vaccination coverage lowered the outbreak's peak.

Economic Burden of SARS-CoV-2 Patients with Multi-Morbidity: A Systematic Review Protocol

Economic burden issues in SARS-CoV-2 patients with underlying co-morbidities are enormous resources for patient treatment and management. The uncertainty costs for clinical management render the healthcare system catatonic and incurs deficits in national annual budgets. This article focuses on systematic steps towards selecting and evaluating literature to uncover gaps and ways to help healthcare stakeholders optimize resources in treating and managing COVID-19 patients with multi-morbidity. A systematic review of all COVID-19 treatment procedures with co-morbidities or multi-morbidity for the period from 2019 to 2022 was conducted. The search includes studies describing treatment costs associated with multi- or co-morbidity cases for infected patients and, if concurrently reported, determining recurring expenses. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Galbraith plots and I2 statistics will be deployed to assess heterogeneity and to identify potential sources. A backward elimination process will be applied in the regression modelling procedure. Based on the number of studies retrieved and their sample size, the subgroup analysis will be stratified on participant disease category, associated total costs, and degree of freedom in cost estimation. These studies were registered in the PROSPERO registry (ID: CRD42022323071).

Coronavirus Host Genomics Study: South Africa (COVIGen-SA)

Host genetic factors are known to modify the susceptibility, severity, and outcomes of COVID-19 and vary across populations. However, continental Africans are yet to be adequately represented in such studies despite the importance of genetic factors in understanding Africa's response to the pandemic. We describe the development of a research resource for coronavirus host genomics studies in South Africa known as COVIGen-SA-a multicollaborator strategic partnership designed to provide harmonised demographic, clinical, and genetic information specific to Black South Africans with COVID-19. Over 2,000 participants have been recruited to date. Preliminary results on 1,354 SARS-CoV-2 positive participants from four participating studies showed that 64.7% were female, 333 had severe disease, and 329 were people living with HIV. Through this resource, we aim to provide insights into host genetic factors relevant to African-ancestry populations, using both genome-wide association testing and targeted sequencing of important genomic loci. This project will promote and enhance partnerships, build skills, and develop resources needed to address the COVID-19 burden and associated risk factors in South African communities.

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology - A Proof-of-Concept Model Development for Remdesivir

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. The disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

Remdesivir Reduces Mortality in Hemato-Oncology Patients with COVID-19

Introduction

Remdesivir is the first agent with proven clinical efficacy against coronavirus disease 2019 (COVID-19); however, its benefit is associated with early use, and its efficacy has been poorly studied in patients with hemato-oncological diseases, who have an increased risk of a severe course of infection. This study aimed to assess the effects of remdesivir on mortality, mechanical ventilation, and the duration of hospitalization in both the general population and in patients with hemato-oncological diseases.

Materials and Methods

Longitudinal data for 4287 patients with confirmed COVID-19 were analyzed, including a subset of 200 individuals with hemato-oncological diseases. In total, 1285 (30.0%) patients received remdesivir, while the remaining patients were treated with other methods. Survival statistics for the 14- and 30-day observation time points were calculated using non-parametric and multivariate Cox models.

Results

Mortality for the 14- and 30-day observation time points was notably lower among patients receiving remdesivir (7.2% vs 11.6%, p < 0.001 and 12.7% vs 16.0, p = 0.005, respectively); however, in multivariate models adjusted for age, sex, lung involvement, and lactate dehydrogenase and interleukin-6 levels, the administration of remdesivir did not reduce patient mortality at either the 14-day or 30-day time points. Among patients with haemato-oncological disease, significant survival benefit was observed at 14 and 30 days for patients treated with remdesivir (11.3% vs.16.7% and 24.2% vs 26.1%, respectively; p < 0.001). A favorable effect of remdesivir was also noted for the 14-day time point in multivariate survival analysis (HR:4.03 [95% confidence interval:1.37-11.88]; p = 0.01).

Conclusion

Remdesivir significantly reduced the early mortality rate in COVID-19 patients with comorbid hemato-oncological disease, which emphasizes the need to administer this agent to immunosuppressed patients.

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

OBJECTIVE

To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19.

DESIGN

Two stage individual participant data meta-analysis.

SETTING

Secondary and tertiary care.

PARTICIPANTS

46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021.

DATA SOURCES

Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge.

MODEL SELECTION AND ELIGIBILITY CRITERIA

Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor.

METHODS

Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters.

MAIN OUTCOME MEASURES

30 day mortality or in-hospital mortality.

RESULTS

Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28).

CONCLUSION

The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

Self-Reported Long COVID in the General Population: Sociodemographic and Health Correlates in a Cross-National Sample

We aimed to gain knowledge of possible sociodemographic predictors of long COVID and whether long COVID was associated with health outcomes almost two years after the pandemic outbreak. There were 1649 adults who participated in the study by completing a cross-sectional online survey disseminated openly in Norway, the UK, the USA, and Australia between November 2021 and January 2022. Participants were defined as having long COVID based on self-reports that they had been infected by COVID-19 and were experiencing long-lasting COVID symptoms. Logistic regression analyses were used to examine possible sociodemographic predictors, and multivariate analysis of variance was used to examine whether long COVID status was associated with health outcomes. None of the sociodemographic variables was significantly associated with reporting long COVID. Having long COVID was associated with higher levels of psychological distress, fatigue, and perceived stress. The effect of long COVID on health outcomes was greater among men than among women. In conclusion, long COVID appeared across sociodemographic groups. People with long COVID reported worsened health outcomes compared to those who had had COVID-19 but without long-term symptoms. Men experiencing long COVID appear to be particularly vulnerable to experiencing poorer health outcomes; health services may pay extra attention to potentially unnoticed needs for support among men experiencing long COVID.

The Aryl Hydrocarbon Receptor Modulates T Follicular Helper Cell Responses to Influenza Virus Infection in Mice

T follicular helper (Tfh) cells support Ab responses and are a critical component of adaptive immune responses to respiratory viral infections. Tfh cells are regulated by a network of signaling pathways that are controlled, in part, by transcription factors. The aryl hydrocarbon receptor (AHR) is an environment-sensing transcription factor that modulates many aspects of adaptive immunity by binding a range of small molecules. However, the contribution of AHR signaling to Tfh cell differentiation and function is not known. In this article, we report that AHR activation by three different agonists reduced the frequency of Tfh cells during primary infection of C57BL/6 mice with influenza A virus (IAV). Further, using the high-affinity and AHR-specific agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin, we show that AHR activation reduced Tfh cell differentiation and T cell-dependent B cell responses. Using conditional AHR knockout mice, we demonstrated that alterations of Tfh cells and T cell-dependent B cell responses after AHR activation required the AHR in T cells. AHR activation reduced the number of T follicular regulatory (Tfr) cells; however, the ratio of Tfr to Tfh cells was amplified. These alterations to Tfh and Tfr cells during IAV infection corresponded with differences in expression of BCL6 and FOXP3 in CD4⁺ T cells and required the AHR to have a functional DNA-binding domain. Overall, these findings support that the AHR modulates Tfh cells during viral infection, which has broad-reaching consequences for understanding how environmental factors contribute to variation in immune defenses against infectious pathogens, such as influenza and severe acute respiratory syndrome coronavirus.

Anti-inflammatory effects of curcumin-loaded niosomes on respiratory syncytial virus infection in a mice model

Respiratory syncytial virus (RSV) is the most common cause of lower respiratory tract infection in paediatrics. While antivirals are apparent candidates to treat RSV-induced diseases, they have not yet met expectations and have remained in infancy. There is growing evidence to suggest that modulating the exacerbated inflammation during RSV infection can improve disease outcome. Curcumin-loaded niosomes have anti-inflammatory effects against RSV-induced respiratory disease by reducing immune cells' infiltration and inflammatory cytokines' production. This study evaluated the effects of curcumin-loaded niosomes on RSV-induced immunopathology in a mice model. Curcumin-loaded niosomes were prepared using the thin-film hydration method and characterized in vitro. Female Balb/c mice were infected by RSV-A2 and treated daily with curcumin-loaded niosomes. The potential anti-inflammatory effects of curcumin-loaded niosomes were evaluated on day 5 after infection. Using curcumin-loaded niosomes decreased immune cell influx and the inflammatory mediators (MIP-1α, TNF-α and IFN-γ) production in the lung, resulting in alleviated lung pathology following RSV infection. These findings indicate that curcumin-loaded niosomes have anti-inflammatory potential and could be a promising candidate to alleviate RSV-associated immunopathology.

The Genomic Profile Associated with Risk of Severe Forms of COVID-19 in Amazonian Native American Populations

Genetic factors associated with COVID-19 disease outcomes are poorly understood. This study aimed to associate genetic variants in the SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, XCR1, and ABO genes with the risk of severe forms of COVID-19 in Amazonian Native Americans, and to compare the frequencies with continental populations. The study population was composed of 64 Amerindians from the Amazon region of northern Brazil. The difference in frequencies between the populations was analyzed using Fisher’s exact test, and the results were significant when p ≤ 0.05. We investigated 64 polymorphisms in 7 genes; we studied 47 genetic variants that were new or had impact predictions of high, moderate, or modifier. We identified 15 polymorphisms with moderate impact prediction in 4 genes (ABO, CXCR6, FYCO1, and SLC6A20). Among the variants analyzed, 18 showed significant differences in allele frequency in the NAM population when compared to others. We reported two new genetic variants with modifier impact in the Amazonian population that could be studied to validate the possible associations with COVID-19 outcomes. The genomic profile of Amazonian Native Americans may be associated with protection from severe forms of COVID-19. This work provides genomic data that may help forthcoming studies to improve COVID-19 outcomes.