COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms.

Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusions: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

A pseudovirus-based platform to measure neutralizing antibodies in Mexico using SARS-CoV-2 as proof-of-concept.

The gold-standard method to evaluate a functional antiviral immune response is to titer neutralizing antibodies (NAbs) against a viral pathogen. This is historically performed using an in vitro assay of virus-mediated infection, which requires BSL-3 facilities. As these are insufficient in Latin American countries, including Mexico, scant information is obtained locally about viral pathogens NAb, using a functional assay. An alternative solution to using a BSL-3 assay with live virus is to use a BSL-2-safe assay with a non-replicative pseudovirus. Pseudoviral particles can be engineered to display a selected pathogen's entry protein on their surface, and to deliver a reporter gene into target cells upon transduction. Here we comprehensively describe the first development of a BSL-2 safe NAbs-measuring functional assay in Mexico, based on the production of pseudotyped lentiviral particles. As proof-of-concept, the assay is based on Nanoluc luciferase-mediated luminescence measurements from target cells transduced with SARS-CoV-2 Spike-pseudotyped lentiviral particles. We applied the optimized assay in a BSL-2 facility to measure NAbs in 65 serum samples, which evidenced the assay with 100% sensitivity, 86.6% specificity and 96% accuracy. Overall, this is the first report of a BSL-2 safe pseudovirus-based functional assay developed in Mexico to measure NAbs, and a cornerstone methodology necessary to measure NAbs with a functional assay in limited resources settings.

Homogeneous surrogate virus neutralization assay to rapidly assess neutralization activity of anti-SARS-CoV-2 antibodies.

The COVID-19 pandemic triggered the development of numerous diagnostic tools to monitor infection and to determine immune response. Although assays to measure binding antibodies against SARS-CoV-2 are widely available, more specific tests measuring neutralization activities of antibodies are immediately needed to quantify the extent and duration of protection that results from infection or vaccination. We previously developed a 'Serological Assay based on a Tri-part split-NanoLuc® (SATiN)' to detect antibodies that bind to the spike (S) protein of SARS-CoV-2. Here, we expand on our previous work and describe a reconfigured version of the SATiN assay, called Neutralization SATiN (Neu-SATiN), which measures neutralization activity of antibodies directly from convalescent or vaccinated sera. The results obtained with our assay and other neutralization assays are comparable but with significantly shorter preparation and run time for Neu-SATiN. As the assay is modular, we further demonstrate that Neu-SATiN enables rapid assessment of the effectiveness of vaccines and level of protection against existing SARS-CoV-2 variants of concern and can therefore be readily adapted for emerging variants.

Non-Coding RNAs in Rheumatoid Arthritis: Implications for Biomarker Discovery.

Recent advances in gene expression analysis techniques and increased access to technologies such as microarrays, qPCR arrays, and next-generation sequencing, in the last decade, have led to increased awareness of the complexity of the inflammatory responses that lead to pathology. This finding is also the case for rheumatic diseases, importantly and specifically, rheumatoid arthritis (RA). The coincidence in major genetic and epigenetic regulatory events leading to RA's inflammatory state is now well-recognized. Research groups have characterized the gene expression profile of early RA patients and identified a group of miRNAs that is particularly abundant in the early stages of the disease and miRNAs associated with treatment responses. In this perspective, we summarize the current state of RNA-based biomarker discovery and the context of technology adoption/implementation due to the COVID-19 pandemic. These advances have great potential for clinical application and could provide preclinical disease detection, follow-up, treatment targets, and biomarkers for treatment response monitoring.

Non-Coding RNAs in Rheumatoid Arthritis: Implications for Biomarker Discovery

Recent advances in gene expression analysis techniques and increased access to technologies such as microarrays, qPCR arrays, and next-generation sequencing, in the last decade, have led to increased awareness of the complexity of the inflammatory responses that lead to pathology. This finding is also the case for rheumatic diseases, importantly and specifically, rheumatoid arthritis (RA). The coincidence in major genetic and epigenetic regulatory events leading to RA's inflammatory state is now well-recognized. Research groups have characterized the gene expression profile of early RA patients and identified a group of miRNAs that is particularly abundant in the early stages of the disease and miRNAs associated with treatment responses. In this perspective, we summarize the current state of RNA-based biomarker discovery and the context of technology adoption/implementation due to the COVID-19 pandemic. These advances have great potential for clinical application and could provide preclinical disease detection, follow-up, treatment targets, and biomarkers for treatment response monitoring.

Evaluation of a Novel Multiplex Platform for Simultaneous Detection of IgG Antibodies Against the 4 Main SARS-CoV-2 Antigens.

BACKGROUND: Numerous serology assays are available for detection of SARS-CoV-2 antibodies but are limited in that only 1 or 2 target antigen(s) can be tested at a time. Here, we describe a novel multiplex assay that simultaneously detects and quantifies IgG antibodies to SARS-CoV-2 antigens, spike (S), nucleocapsid (N), receptor-binding domain (RBD), and N-terminal domain (NTD) in a single well. METHODS: Sensitivity was determined using samples (n = 124) from confirmed SARS-CoV-2 RT-PCR positive individuals. Prepandemic (n = 100) and non-COVID respiratory infection positive samples (n = 100) were used to evaluate specificity. Samples were analyzed using COVID-19 IgG multiplex serology assay from Meso Scale Discovery (MSD) and using commercial platforms from Abbott, EUROIMMUN, and Siemens. RESULTS: At >14 days post-PCR, MSD assay displayed >98.0% sensitivity [S 100% (95% CI 98.0%-100.0%); N 98.0% (95% CI 97.2%-98.9%); RBD 94.1% (95% CI 92.6%-95.6%); NTD 98.0% (95% CI, 97.2%-98.9%)] and 99% specificity (95% CI 99.3%-99.7%) for antibodies to all 4 antigens. Parallel assessment of antibodies to more than 1 antigen improved the sensitivity to 100% (95% CI 98.0%-100.0%) while maintaining 98% (95% CI 97.6%-98.4%) specificity regardless of the combinations used. When AU/mL concentrations of IgG antibodies from the MSD assay were compared against the corresponding IgG signals acquired from the single target commercial assays, the following correlations were observed: Abbott (vs MSD N, R2 = 0.73), Siemens (vs MSD RBD, R2 = 0.92), and EUROIMMUN (vs MSD S, R2 = 0.82). CONCLUSION: MSD assay offers an accurate and a comprehensive assessment of SARS-CoV-2 antibodies with higher sensitivity and equivalent specificity compared to the commercial IgG serology assays.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach.

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Evaluation of a Surrogate Enzyme-Linked Immunosorbent Assay-Based Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) cPass Neutralization Antibody Detection Assay and Correlation With Immunoglobulin G Commercial Serology Assays.

CONTEXT.­: Emerging evidence shows correlation between the presence of neutralization antibodies (nAbs) and protective immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently available commercial serology assays lack the ability to specifically identify nAbs. An enzyme-linked immunosorbent assay-based nAb assay (GenScript cPass neutralization antibody assay) has recently received emergency use authorization from the Food and Drug Administration. OBJECTIVE.­: To evaluate the performance characteristics of this assay and compare and correlate it with the commercial assays that detect SARS-CoV-2-specific immunoglobulin G (IgG). DESIGN.­: Specimens from SARS-COV-2 infected patients (n = 124), healthy donors obtained prepandemic (n = 100), and patients with non-coronavirus disease 2019 (COVID-19) respiratory infections (n = 92) were analyzed using this assay. Samples with residual volume were also tested on 3 commercial serology platforms (Abbott, Euroimmun, Siemens). Twenty-eight randomly selected specimens from patients with COVID-19 and 10 healthy controls were subjected to a plaque reduction neutralization test. RESULTS.­: The cPass assay exhibited 96.1% (95% CI, 94.9%-97.3%) sensitivity (at >14 days post-positive PCR), 100% (95% CI, 98.0%-100.0%) specificity, and zero cross-reactivity for the presence of non-COVID-19 respiratory infections. When compared with the plaque reduction assay, 97.4% (95% CI, 96.2%-98.5%) qualitative agreement and a positive correlation (R2 = 0.76) was observed. Comparison of IgG signals from each of the commercial assays with the nAb results from plaque reduction neutralization test/cPass assays displayed greater than 94.7% qualitative agreement and correlations with R2 = 0.43/0.68 (Abbott), R2 = 0.57/0.85 (Euroimmun), and R2 = 0.39/0.63 (Siemens), respectively. CONCLUSIONS.­: The combined data support the use of cPass assay for accurate detection of the nAb response. Positive IgG results from commercial assays associated reasonably with nAbs presence and can serve as a substitute.

Neutralization assay with SARS-CoV-1 and SARS-CoV-2 spike pseudotyped murine leukemia virions.

BACKGROUND: Virus neutralization by antibodies is an important prognostic factor in many viral diseases. To easily and rapidly measure titers of neutralizing antibodies in serum or plasma, we developed pseudovirion particles composed of the spike glycoprotein of SARS-CoV-2 incorporated onto murine leukemia virus capsids and a modified minimal murine leukemia virus genome encoding firefly luciferase. This assay design is intended for use in laboratories with biocontainment level 2 and therefore circumvents the need for the biocontainment level 3 that would be required for replication-competent SARS-CoV-2 virus. To validate the pseudovirion assay, we set up comparisons with other available antibody tests including those from Abbott, Euroimmun and Siemens, using archived, known samples. RESULTS: 11 out of 12 SARS-CoV-2-infected patient serum samples showed neutralizing activity against SARS-CoV-2-spike pseudotyped MLV viruses, with neutralizing titers-50 (NT50) that ranged from 1:25 to 1:1,417. Five historical samples from patients hospitalized for severe influenza infection in 2016 tested negative in the neutralization assay (NT50 < 25). Three serum samples with high neutralizing activity against SARS-CoV-2/MLV pseudoviruses showed no detectable neutralizing activity (NT50 < 25) against SARS-CoV-1/MLV pseudovirions. We also compared the semiquantitative Siemens SARS-CoV-2 IgG test, which measures binding of IgG to recombinantly expressed receptor binding domain of SARS-CoV-2 spike glycoprotein with the neutralization titers obtained in the pseudovirion assay and the results show high concordance between the two tests (R2 = 0.9344). CONCLUSIONS: SARS-CoV-2 spike/MLV pseudovirions provide a practical means of assessing neutralizing activity of antibodies in serum or plasma from infected patients under laboratory conditions consistent with biocontainment level 2. This assay offers promise also in evaluating immunogenicity of spike glycoprotein-based candidate vaccines in the near future.

Probability-Based Estimates of Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence and Detection Fraction, Utah, USA.

We aimed to generate an unbiased estimate of the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 4 urban counties in Utah, USA. We used a multistage sampling design to randomly select community-representative participants >12 years of age. During May 4-June 30, 2020, we collected serum samples and survey responses from 8,108 persons belonging to 5,125 households. We used a qualitative chemiluminescent microparticle immunoassay to detect SARS-CoV-2 IgG in serum samples. We estimated the overall seroprevalence to be 0.8%. The estimated seroprevalence-to-case count ratio was 2.5, corresponding to a detection fraction of 40%. Only 0.2% of participants from whom we collected nasopharyngeal swab samples had SARS-CoV-2-positive reverse transcription PCR results. SARS-CoV-2 antibody prevalence during the study was low, and prevalence of PCR-positive cases was even lower. The comparatively high SARS-CoV-2 detection rate (40%) demonstrates the effectiveness of Utah's testing strategy and public health response.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19.

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).

Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19.

Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. This cross-sectional study used targeted metabolomics to identify potential COVID-19 biomarkers that predicted the course of the illness by assessing 110 endogenous plasma metabolites from individuals admitted to a local hospital for diagnosis/treatment. Patients were classified into four groups (≈ 40 each) according to standard polymerase chain reaction (PCR) COVID-19 testing and disease course: PCR-/controls (i.e., non-COVID controls), PCR+/not-hospitalized, PCR+/hospitalized, and PCR+/intubated. Blood samples were collected within 2 days of admission/PCR testing. Metabolite concentration data, demographic data and clinical data were used to propose biomarkers and develop optimal regression models for the diagnosis and prognosis of COVID-19. The area under the receiver operating characteristic curve (AUC; 95% CI) was used to assess each models' predictive value. A panel that included the kynurenine: tryptophan ratio, lysoPC a C26:0, and pyruvic acid discriminated non-COVID controls from PCR+/not-hospitalized (AUC = 0.947; 95% CI 0.931-0.962). A second panel consisting of C10:2, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95% CI 0.968-0.983). Only lysoPC a C28:0 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95% CI 0.736-0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.

Clinical Factors Associated with COVID-19 Severity in Mexican Patients: Cross-Sectional Analysis from a Multicentric Hospital Study.

(1) Background: Latin America has been harshly hit by SARS-CoV-2, but reporting from this region is still incomplete. This study aimed at identifying and comparing clinical characteristics of patients with COVID-19 at different stages of disease severity. (2) Methods: Cross-sectional multicentric study. Individuals with nasopharyngeal PCR were categorized into four groups: (1) negative, (2) positive, not hospitalized, (3) positive, hospitalized with/without supplementary oxygen, and (4) positive, intubated. Clinical and laboratory data were compared, using group 1 as the reference. Multivariate multinomial logistic regression was used to compare adjusted odds ratios. (3) Results: Nine variables remained in the model, explaining 76% of the variability. Men had increased odds, from 1.90 (95%CI 0.87-4.15) in the comparison of 2 vs. 1, to 3.66 (1.12-11.9) in 4 vs. 1. Diabetes and obesity were strong predictors. For diabetes, the odds for groups 2, 3, and 4 were 1.56 (0.29-8.16), 12.8 (2.50-65.8), and 16.1 (2.87-90.2); for obesity, these were 0.79 (0.31-2.05), 3.38 (1.04-10.9), and 4.10 (1.16-14.4), respectively. Fever, myalgia/arthralgia, cough, dyspnea, and neutrophilia were associated with the more severe COVID-19 group. Anosmia/dysgeusia were more likely to occur in group 2 (25.5; 2.51-259). (4) Conclusion: The results point to relevant differences in clinical and laboratory features of COVID-19 by level of severity that can be used in medical practice.

Decreasing median age of COVID-19 cases in the United States-Changing epidemiology or changing surveillance?

BACKGROUND: Understanding and monitoring the demographics of SARS-CoV-2 infection can inform strategies for prevention. Surveillance monitoring has suggested that the age distribution of people infected with SARS-CoV-2 has changed since the pandemic began, but no formal analysis has been performed. METHODS: Retrospective review of SARS-CoV-2 molecular testing results from a national reference laboratory was performed. Result distributions by age and positivity were compared between early period (March-April 2020) and late periods (June-July 2020) of the COVID-19 pandemic. Additionally, a sub-analysis compared changing age distributions between inpatients and outpatients. RESULTS: There were 277,601 test results of which 19320 (7.0%) were positive. The median age of infected people declined over time (p < 0.0005). In March-April, the median age of positive people was 40.8 years (Interquartile range (IQR): 29.0-54.1). In June-July, the median age of positive people was 35.8 years (IQR: 24.0-50.2). The positivity rate of patients under 50 increased from 6.0 to 10.6 percent and the positivity rate for those over 50 decreased from 6.3 to 5.0 percent between the early and late periods. The trend was only observed for outpatient populations. CONCLUSIONS: We confirm that there is a trend toward decreasing age among persons with laboratory-confirmed SARS-CoV-2 infection, but that these trends seem to be specific to the outpatient population. Overall, this suggests that observed age-related trends are driven by changes in testing patterns rather than true changes in the epidemiology of SARS-CoV-2 infection. This calls for caution in interpretation of routine surveillance data until testing patterns stabilize.

Neutralization Assay with SARS-CoV-1 and SARS-CoV-2 Spike Pseudotyped Murine Leukemia Virions.

Antibody neutralization is an important prognostic factor in many viral diseases. To easily and rapidly measure titers of neutralizing antibodies in serum or plasma, we developed pseudovirion particles composed of the spike glycoprotein of SARS-CoV-2 incorporated onto murine leukemia virus capsids and a modified minimal MLV genome encoding firefly luciferase. These pseudovirions provide a practical means of assessing immune responses under laboratory conditions consistent with biocontainment level 2.

COVID-19 severity and mortality in patients with chronic lymphocytic leukemia: a joint study by ERIC, the European Research Initiative on CLL, and CLL Campus.

Chronic lymphocytic leukemia (CLL) is a disease of the elderly, characterized by immunodeficiency. Hence, patients with CLL might be considered more susceptible to severe complications from COVID-19. We undertook this retrospective international multicenter study to characterize the course of COVID-19 in patients with CLL and identify potential predictors of outcome. Of 190 patients with CLL and confirmed COVID-19 diagnosed between 28/03/2020 and 22/05/2020, 151 (79%) presented with severe COVID-19 (need of oxygen and/or intensive care admission). Severe COVID-19 was associated with more advanced age (≥65 years) (odds ratio 3.72 [95% CI 1.79-7.71]). Only 60 patients (39.7%) with severe COVID-19 were receiving or had recent (≤12 months) treatment for CLL at the time of COVID-19 versus 30/39 (76.9%) patients with mild disease. Hospitalization rate for severe COVID-19 was lower (p < 0.05) for patients on ibrutinib versus those on other regimens or off treatment. Of 151 patients with severe disease, 55 (36.4%) succumbed versus only 1/38 (2.6%) with mild disease; age and comorbidities did not impact on mortality. In CLL, (1) COVID-19 severity increases with age; (2) antileukemic treatment (particularly BTK inhibitors) appears to exert a protective effect; (3) age and comorbidities did not impact on mortality, alluding to a relevant role of CLL and immunodeficiency.