Enhancing the sensitivity of rapid antigen detection test (RADT) of different SARS-CoV-2 variants and lineages using fluorescence-labeled antibodies and a fluorescent meter.

RT-qPCR is considered the gold standard for diagnosis of COVID-19; however, it is laborious, time-consuming, and expensive. RADTs have evolved recently as relatively inexpensive methods to address these shortcomings, but their performance for detecting different SARS-COV-2 variants remains limited. RADT test performance could be enhanced using different antibody labeling and signal detection techniques. Here, we aimed to evaluate the performance of two antigen RADTs for detecting different SARS-CoV-2 variants: (i) the conventional colorimetric RADT (Ab-conjugated with gold beads); and (ii) the new Finecare™ RADT (Ab-coated fluorescent beads). Finecare™ is a meter used for the detection of a fluorescent signal. 187 frozen nasopharyngeal swabs collected in Universal transport (UTM) that are RT-qPCR positive for different SARS-CoV-2 variants were selected, including Alpha (n = 60), Delta (n = 59), and Omicron variants (n = 108). Sixty flu and 60 RSV-positive samples were included as negative controls (total sample number = 347). The conventional RADT showed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 62.4% (95%CI: 54-70), 100% (95%CI: 97-100), 100% (95%CI: 100-100), and 58% (95%CI: 49-67), respectively. These measurements were enhanced using the Finecare™ RADT: sensitivity, specificity, PPV, and NPV were 92.6% (95%CI: 89.08-92.3), 96% (95%CI: 96-99.61), 98% (95%CI: 89-92.3), and 85% (95%CI: 96-99.6) respectively. The sensitivity of both RADTs could be greatly underestimated because nasopharyngeal swab samples collected UTM and stored at -80 °C were used. Despite that, our results indicate that the Finecare™ RADT is appropriate for clinical laboratory and community-based surveillance due to its high sensitivity and specificity.

The retrospective study of the metabolic patterns of BCG-vaccination in type-2 diabetic individuals in COVID-19 infection.

Background: The cross-protective nature of Bacillus Calmette-Guerin (BCG) vaccine against SARS-CoV-2 virus was previously suggested, however its effect in COVID-19 patients with type 2 diabetes (T2D) and the underlying metabolic pathways has not been addressed. This study aims to investigate the difference in the metabolomic patterns of type 2 diabetic patients with BCG vaccination showing different severity levels of COVID-19 infection. Methods: Sixty-seven COVID-19 patients were categorized into diabetic and non-diabetic individuals who had been previously vaccinated or not with BCG vaccination. Targeted metabolomics were performed from serum samples from all patients using tandem mass spectrometry. Statistical analysis included multivariate and univariate models. Results: Data suggested that while BCG vaccination may provide protection for individuals who do not have diabetes, it appears to be linked to more severe COVID-19 symptoms in T2D patients (p = 0.02). Comparing the metabolic signature of BCG vaccinated T2D individuals to non-vaccinated counterparts revealed that amino acid (sarcosine), cholesterol esters (CE 20:0, 20:1, 22:2), carboxylic acid (Aconitic acid) were enriched in BCG vaccinated T2D patients, whereas spermidine, glycosylceramides (Hex3Cer(d18:1_22:0), Hex2Cer(d18:1/22:0), HexCer(d18:1/26:1), Hex2Cer(d18:1/24:0), HexCer(d18:1/22:0) were higher in BCG vaccinated non- T2D patients. Furthermore, data indicated a decrease in sarcosine synthesis from glycine and choline and increase in spermidine synthesis in the BCG vaccinated cohort in T2D and non-T2D groups, respectively. Conclusion: This pilot study suggests increased severity of COVID-19 in BCG vaccinated T2D patients, which was marked by decreased sarcosine synthesis, perhaps via lower sarcosine-mediated removal of viral antigens.

Long-term COVID-19 booster effectiveness by infection history and clinical vulnerability and immune imprinting: a retrospective population-based cohort study.

BACKGROUND: Long-term effectiveness of COVID-19 mRNA boosters in populations with different previous infection histories and clinical vulnerability profiles is inadequately understood. We aimed to investigate the effectiveness of a booster (third dose) vaccination against SARS-CoV-2 infection and against severe, critical, or fatal COVID-19, relative to that of primary-series (two-dose) vaccination over a follow-up duration of 1 year. METHODS: This observational, matched, retrospective, cohort study was done on the population of Qatar in people with different immune histories and different clinical vulnerability to infection. The source of data are Qatar's national databases for COVID-19 laboratory testing, vaccination, hospitalisation, and death. Associations were estimated using inverse-probability-weighted Cox proportional-hazards regression models. The primary outcome of the study is the effectiveness of COVID-19 mRNA boosters against infection and against severe COVID-19. FINDINGS: Data were obtained for 2 228 686 people who had received at least two vaccine doses starting from Jan 5, 2021, of whom 658 947 (29·6%) went on to receive a third dose before data cutoff on Oct 12, 2022. There were 20 528 incident infections in the three-dose cohort and 30 771 infections in the two-dose cohort. Booster effectiveness relative to primary series was 26·2% (95% CI 23·6-28·6) against infection and 75·1% (40·2-89·6) against severe, critical, or fatal COVID-19, during 1-year follow-up after the booster. Among people clinically vulnerable to severe COVID-19, effectiveness was 34·2% (27·0-40·6) against infection and 76·6% (34·5-91·7) against severe, critical, or fatal COVID-19. Effectiveness against infection was highest at 61·4% (60·2-62·6) in the first month after the booster but waned thereafter and was modest at only 15·5% (8·3-22·2) by the sixth month. In the seventh month and thereafter, coincident with BA.4/BA.5 and BA.2·75* subvariant incidence, effectiveness was progressively negative albeit with wide CIs. Similar patterns of protection were observed irrespective of previous infection status, clinical vulnerability, or type of vaccine (BNT162b2 vs mRNA-1273). INTERPRETATION: Protection against omicron infection waned after the booster, and eventually suggested a possibility for negative immune imprinting. However, boosters substantially reduced infection and severe COVID-19, particularly among individuals who were clinically vulnerable, affirming the public health value of booster vaccination. FUNDING: The Biomedical Research Program and the Biostatistics, Epidemiology, and the Biomathematics Research Core (both at Weill Cornell Medicine-Qatar), Ministry of Public Health, Hamad Medical Corporation, Sidra Medicine, Qatar Genome Programme, and Qatar University Biomedical Research Center.

Detection of Antinuclear Antibodies Targeting Intracellular Signal Transduction, Metabolism, Apoptotic Processes and Cell Death in Critical COVID-19 Patients.

Background and Objectives: The heterogeneity of the coronavirus disease of 2019 (COVID-19) lies within its diverse symptoms and severity, ranging from mild to lethal. Acute respiratory distress syndrome (ARDS) is a leading cause of mortality in COVID-19 patients, characterized by a hyper cytokine storm. Autoimmunity is proposed to occur as a result of COVID-19, given the high similarity of the immune responses observed in COVID-19 and autoimmune diseases. Here, we investigate the level of autoimmune antibodies in COVID-19 patients with different severities. Results: Initial screening for antinuclear antibodies (ANA) IgG using ELISA revealed that 1.58% (2/126) and 4% (5/126) of intensive care unit (ICU) COVID-19 cases expressed strong and moderate ANA levels, respectively. An additional sample was positive with immunofluorescence assays (IFA) screening. However, all the non-ICU cases (n=273) were ANA negative using both assays. Samples positive for ANA were further confirmed with large-scale autoantibody screening by phage immunoprecipitation-sequencing (PhIP-Seq). The majority of the ANA-positive samples showed "speckled" ANA pattern by microscopy and revealed autoantibody specificities that targeted proteins involved in intracellular signal transduction, metabolism, apoptotic processes, and cell death by PhIP-Seq; further denoting reactivity to nuclear and cytoplasmic antigens. Conclusion: Our results further support the notion of routine screening for autoimmune responses in COVID-19 patients, which might help improve disease prognosis and patient management. Further, results provide compelling evidence that ANA-positive individuals should be excluded from being donors for convalescent plasma therapy in the context of COVID-19.

Will Host Genetics Affect the Response to SARS-CoV-2 Vaccines? Historical Precedents.

Recent progress in genomics and bioinformatics technologies have allowed for the emergence of immunogenomics field. This intersection of immunology and genetics has broadened our understanding of how the immune system responds to infection and vaccination. While the immunogenetic basis of the huge clinical variability in response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is currently being extensively studied, the host genetic determinants of SARS-CoV-2 vaccines remain largely unknown. Previous reports evidenced that vaccines may not protect all populations or individuals equally, due to multiple host- and vaccine-specific factors. Several studies on vaccine response to measles, rubella, hepatitis B, smallpox, and influenza highlighted the contribution of genetic mutations or polymorphisms in modulating the innate and adaptive immunity following vaccination. Specifically, genetic variants in genes encoding virus receptors, antigen presentation, cytokine production, or related to immune cells activation and differentiation could influence how an individual responds to vaccination. Although such knowledge could be utilized to generate personalized vaccine strategies to optimize the vaccine response, studies in this filed are still scarce. Here, we briefly summarize the scientific literature related to the immunogenetic determinants of vaccine-induced immunity, highlighting the possible role of host genetics in response to SARS-CoV-2 vaccines as well.

Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications.

COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from 50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients.

Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity.

Cross-reactivity between different human coronaviruses (HCoVs) might contribute to COVID-19 outcomes. Here, we aimed to predict conserved peptides among different HCoVs that could elicit cross-reacting B cell and T cell responses. Three hundred fifty-one full-genome sequences of HCoVs, including SARS-CoV-2 (51), SARS-CoV-1 (50), MERS-CoV (50), and common cold species OC43 (50), NL63 (50), 229E (50), and HKU1 (50) were downloaded aligned using Geneious Prime 20.20. Identification of epitopes in the conserved regions of HCoVs was carried out using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. Further, we identified sequences that bind multiple common MHC and modeled the three-dimensional structures of the protein regions. The search yielded 73 linear and 35 discontinuous epitopes. A total of 16 B-cell and 19 T-cell epitopes were predicted through a comprehensive bioinformatic screening of conserved regions derived from HCoVs. The 16 potentially cross-reactive B-cell epitopes included 12 human proteins and four viral proteins among the linear epitopes. Likewise, we identified 19 potentially cross-reactive T-cell epitopes covering viral proteins. Interestingly, two conserved regions: LSFVSLAICFVIEQF (NSP2) and VVHSVNSLVSSMEVQSL (spike), contained several matches that were described epitopes for SARS-CoV. Most of the predicted B cells were buried within the SARS-CoV-2 protein regions' functional domains, whereas T-cell stretched close to the functional domains. Additionally, most SARS-CoV-2 predicted peptides (80%) bound to different HLA types associated with autoimmune diseases. We identified a set of potential B cell and T cell epitopes derived from the HCoVs that could contribute to different diseases manifestation, including autoimmune disorders.

Reinfections in COVID-19 Patients: Impact of Virus Genetic Variability and Host Immunity.

The COVID-19 pandemic is still posing a devastating threat to social life and economics. Despite the modest decrease in the number of cases during September-November 2020, the number of active cases is on the rise again. This increase was associated with the emergence and spread of the new SARS-CoV-2 variants of concern (VOCs), such as the U.K. (B1.1.7), South Africa (B1.351), Brazil (P1), and Indian (B1.617.2) strains. The rapid spread of these new variants has raised concerns about the multiple waves of infections and the effectiveness of available vaccines. In this review, we discuss SARS-CoV-2 reinfection rates in previously infected and vaccinated individuals in relation to humoral responses. Overall, a limited number of reinfection cases have been reported worldwide, suggesting long protective immunity. Most reinfected patients were asymptomatic during the second episode of infection. Reinfection was attributed to several viral and/or host factors, including (i) underlying immunological comorbidities; (ii) low antibody titers due to the primary infection or vaccination; (iii) rapid decline in antibody response after infection or vaccination; and (iv) reinfection with a different SARS-CoV-2 variant/lineage. Infections after vaccination were also reported on several occasions, but mostly associated with mild or no symptoms. Overall, findings suggest that infection- and vaccine-induced immunity would protect from severe illness, with the vaccine being effective against most VOCs.

Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients.

Introduction: Detection of early metabolic changes in critically-ill coronavirus disease 2019 (COVID-19) patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease management. Methods: Targeted metabolomics of serum samples from 39 COVID-19 patients under IMV in ICU was performed within 48 h of intubation and a week later. A generalized linear model (GLM) was used to identify, at both time points, metabolites and clinical traits that predict the length of stay (LOS) at ICU (short ≤ 14 days/long >14 days) as well as the duration under IMV. All models were initially trained on a set of randomly selected individuals and validated on the remaining individuals in the cohort. Further validation in recently published metabolomics data of COVID-19 severity was performed. Results: A model based on hypoxanthine and betaine measured at first time point was best at predicting whether a patient is likely to experience a short or long stay at ICU [area under curve (AUC) = 0.92]. A further model based on kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate, and C24.0 sphingomyelin, measured 1 week later, accurately predicted the duration of IMV (Pearson correlation = 0.94). Both predictive models outperformed Acute Physiology and Chronic Health Evaluation II (APACHE II) scores and differentiated COVID-19 severity in published data. Conclusion: This study has identified specific metabolites that can predict in advance LOS and IMV, which could help in the management of COVID-19 cases at ICU.

Molecular and biological characterization of influenza A viruses isolated from human fecal samples.

Human influenza viruses are occasionally detected in the stools of influenza patients. OBJECTIVES: Here, we investigated the molecular and biological characteristics of intestinal influenza viruses and their potential role in virus transmission. METHODS: Fecal samples were first screened for the presence of influenza viral RNA using RT-qPCR. Positive fecal samples were subjected to cell culture. Isolated viruses were then sequenced using MiSeq platform. Replication kinetics and receptor binding affinity were also evaluated. RESULTS: Influenza RNA was detected in stool samples of 41% (36/87) of influenza A positive patients. Among the 36 stool samples subjected to viral isolation, 5 showed virus growth. Sequence analysis of isolated viruses revealed two distinct mutation patterns in fecal viruses. Set I viruses was able to replicate to higher titers in cell culture despite the limited number of mutations (6 mutations) compared to set II viruses (>10 mutations). Functional analysis of both sets revealed the ability to replicate efficiently in differentiated human bronchial cells. Receptor binding testing has also demonstrated their ability to bind α 2,3 and α 2,6 sialic acid receptors. CONCLUSION: The ability of fecal influenza viruses to replicate in intestinal cells and human 3D bronchial cells might suggest their possible contribution in virus transmission.

The Spectrum of Antibiotic Prescribing During COVID-19 Pandemic: A Systematic Literature Review.

Objectives: Over the last decades, there has been a significant increase in antimicrobial prescribing and consumption associated with the development of patients' adverse events and antimicrobial resistance (AMR) to the point of becoming a global priority. This study aims at evaluating antibiotic prescribing during COVID-19 pandemic from November 2019 to December 2020. Materials and Methods: A systematic review was conducted primarily through the NCBI database, using PRISMA guidelines to identify relevant literature for the period between November 1, 2019 and December 19, 2020, using the keywords: COVID-19 OR SARS-Cov-2 AND antibiotics restricted to the English language excluding nonclinical articles. Five hundred twenty-seven titles were identified; all articles fulfilling the study criteria were included, 133 through the NCBI, and 8 through Google Scholar with a combined total of 141 studies. The patient's spectrum included all ages from neonates to elderly with all associated comorbidities, including immune suppression. Results: Of 28,093 patients included in the combined studies, 58.7% received antibiotics (16,490/28,093), ranging from 1.3% to 100% coverage. Antibiotics coverage was less in children (57%) than in adults with comorbidities (75%). Broad-spectrum antibiotics were prescribed presumptively without pathogen identifications, which might contribute to adverse outcomes. Conclusions: During the COVID-19 pandemic, there has been a significant and wide range of antibiotic prescribing in patients affected by the disease, particularly in adults with underlying comorbidities, despite the paucity of evidence of associated bacterial infections. The current practice might increase patients' immediate and long-term risks of adverse events, susceptibility to secondary infections as well as aggravating AMR.

Demographic and Clinical Characteristics of Early Travel-Associated COVID-19 Cases.

Background: SARS-CoV-2 continues to claim hundreds of thousands of people's lives. It mostly affects the elderly and those with chronic illness but can also be fatal in younger age groups. This article is the first comprehensive analysis of the epidemiological and clinical outcomes of the travel-associated SARS-CoV-2 cases until April 19, 2020. Methods: Demographic and clinical data of travel-associated SARS-CoV-2 cases were collected for the period between January 16, 2020 and April 19, 2020. More than one hundred and eighty databases were searched, including the World Health Organization (WHO) database, countries' ministries websites, and official media sites. Demographic and clinical data were extracted and analyzed. Results: A total of 1,186 cases from 144 countries meeting the inclusion criteria were reported and included in the analysis. The mean age of the cases was 44 years, with a male to female ratio of 1.6:1. Travel-associated cases originated from more than 40 countries, with China, Italy, and Iran reporting the highest numbers at 208, 225, and 155, respectively. Clinical symptoms varied between patients, with some reporting symptoms during the flights (117 cases; 9.87%). A total of 312 (26.31%) cases were hospitalized, of which 50 cases (4.22%) were fatal. Conclusion: Major gaps exist in the epidemiology and clinical spectrum of the COVID-19 travel-associated cases due to a lack of reporting and sharing data of many counties. The identification and implementation of methodologies for measuring traveler's risk to coronavirus would help in minimizing the spread of the virus, especially in the next waves.

Markers Associated with COVID-19 Susceptibility, Resistance, and Severity.

In December 2019, the latest member of the coronavirus family, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, leading to the outbreak of an unusual viral pneumonia known as coronavirus disease 2019 (COVID-19). COVID-19 was then declared as a pandemic in March 2020 by the World Health Organization (WHO). The initial mortality rate of COVID-19 declared by WHO was 2%; however, this rate has increased to 3.4% as of 3 March 2020. People of all ages can be infected with SARS-CoV-2, but those aged 60 or above and those with underlying medical conditions are more prone to develop severe symptoms that may lead to death. Patients with severe infection usually experience a hyper pro-inflammatory immune reaction (i.e., cytokine storm) causing acute respiratory distress syndrome (ARDS), which has been shown to be the leading cause of death in COVID-19 patients. However, the factors associated with COVID-19 susceptibility, resistance and severity remain poorly understood. In this review, we thoroughly explore the correlation between various host, viral and environmental markers, and SARS-CoV-2 in terms of susceptibility and severity.

Within-Host Diversity of SARS-CoV-2 in COVID-19 Patients With Variable Disease Severities.

Background: The ongoing pandemic of SARS-COV-2 has already infected more than eight million people worldwide. The majority of COVID-19 patients either are asymptomatic or have mild symptoms. Yet, about 15% of the cases experience severe complications and require intensive care. Factors determining disease severity are not yet fully characterized. Aim: Here, we investigated the within-host virus diversity in COVID-19 patients with different clinical manifestations. Methods: We compared SARS-COV-2 genetic diversity in 19 mild and 27 severe cases. Viral RNA was extracted from nasopharyngeal samples and sequenced using the Illumina MiSeq platform. This was followed by deep-sequencing analyses of SARS-CoV-2 genomes at both consensus and sub-consensus sequence levels. Results: Consensus sequences of all viruses were very similar, showing more than 99.8% sequence identity regardless of the disease severity. However, the sub-consensus analysis revealed significant differences in within-host diversity between mild and severe cases. Patients with severe symptoms exhibited a significantly (p-value 0.001) higher number of variants in coding and non-coding regions compared to mild cases. Analysis also revealed higher prevalence of some variants among severe cases. Most importantly, severe cases exhibited significantly higher within-host diversity (mean = 13) compared to mild cases (mean = 6). Further, higher within-host diversity was observed in patients above the age of 60 compared to the younger age group. Conclusion: These observations provided evidence that within-host diversity might play a role in the development of severe disease outcomes in COVID-19 patients; however, further investigations are required to elucidate this association.