Tracking the Genomic Evolution of SARS-CoV-2 for 29 Months in South Korea.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued, with the persistent emergence of variants of concern (VOCs). Therefore, this study aimed to track the genomic evolution of SARS-CoV-2 strains by sequencing the spike protein for 29 months, which accounted for the majority of the COVID-19 pandemic period. A total of 109 swabs from patients with confirmed coronavirus disease 2019 (COVID-19) infection were randomly collected between March 2020 and July 2022. After genomic sequencing, we analyzed the naming systems and phylogenetic trees. Five surge peaks of COVID-19 cases have been reported in South Korea, resulting in 14,000,000 cumulative confirmed cases and 17,000 deaths. Among the sequenced samples, 34 wild-type strains and 75 VOCs, including 4 Alpha, 33 Delta, 2 Epsilon, and 36 Omicron VOCs, were identified. Omicron strains were comprised of 8 BA.1.1 (21 K), 27 BA.2 (21 L), and 1 BA.2.12.1 (22C). Phylogenetic analysis of the identified isolates and representative sequences of SARS-CoV-2 strains revealed clusters that presented the WHO VOCs. Specific or unique mutations for each VOC waxed and waned according to the variant waves. Our findings allowed recognition of the overall trends of SARS-CoV-2 isolates, which implicated replication advantage, immune evasion, and disease management.

Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT–PCR Testing

(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT–PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT–PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT–PCR are used for diagnosis shows the possibility of nearly halving the time required for RT–PCR diagnosis.

Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing.

(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT-PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT-PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT-PCR are used for diagnosis shows the possibility of nearly halving the time required for RT-PCR diagnosis.

Prognostic impacts of soluble immune checkpoint regulators and cytokines in patients with SARS-CoV-2 infection.

Coronavirus disease 2019 (COVID-19) has been a pandemic for the past two years. Predicting patient prognosis is critical. Although immune checkpoints (ICs) were shown to be involved in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, quantitative studies of ICs in clinical practice are limited. In this study, various soluble ICs (sICs) and cytokine levels in patients with SARS-CoV-2 infection at different time points were compared between survivors and deaths; we also examined whether sICs are useful for predicting prognosis. sICs and cytokines were measured in serum samples from 38 patients diagnosed with COVID-19 in the first and second week post-diagnosis. All assays were performed by bead-based multiplexed immunoassay system using Luminex Bio-Plex 200 system. The correlation of sICs and cytokines with laboratory markers was evaluated, and the levels of sICs in survivors were compared with those in deaths. Among the sICs, the second-week levels of soluble cluster of differentiation (sCD27, p = 0.012), sCD40 (p< 0.001), cytotoxic T-lymphocyte-associated protein 4 (sCTLA-4, p< 0.001), herpes virus entry mediator (sHVEM, p = 0.026), and T-cell immunoglobulin and mucin-domain containing-3 (sTIM-3, p = 0.002) were significantly higher in deaths than in survivors. The levels of nine cytokines assessed in the second week of deaths were significantly higher than those in survivors. The sICs sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines chemokine CC motif ligand 2 (CCL2), GM-CSF, IL-10, and IL-8 showed significant positive correlations with the levels of C-reactive protein (CRP) and procalcitonin and were negatively correlated with the absolute lymphocyte count and platelet values. Increased levels of sICs including sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines were significant factors for poor prognosis. sICs, together with cytokines and inflammatory markers, may be useful as prognostic stratification markers in SARS-CoV-2-infected patients.

STANDARD M10 SARS-CoV-2 Assay for Rapid Detection of SARS-CoV-2: Comparison of Four Real-Time PCR Assays.

The demand for assays that can rapidly and accurately detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains high. We evaluated the performance of two rapid real-time reverse transcription polymerase chain reaction (RT-qPCR) assays (STANDARD M10 SARS-CoV-2 and Xpert Xpress SARS-CoV-2) against conventional RT-qPCR assays (STANDARD M nCoV and Allplex SARS-CoV-2) for detecting SARS-CoV-2. A total of 225 swab samples were collected and tested using the four assays. The STANDARD M10 SARS-CoV-2 assay showed 97.4% positive percent agreement (PPA) and 100.0% negative percent agreement (NPA) compared to the STANDARD M nCoV assay and Allplex SARS-CoV-2 assay. STANDARD M10 exhibited high performance except in samples with low viral loads (cycle threshold (Ct) > 30). Xpert Xpress showed PPA and NPA of 100.0% compared to the two conventional RT-qPCR assays. The kappa coefficient (Κ) showed nearly almost perfect agreement between each assay and conventional RT-qPCR assays. The correlations of Ct values between the two rapid RT-qPCR and conventional RT-qPCR assays were >0.8, indicating strong correlations. All included assays could detect SARS-CoV-2 variants, such as the Alpha, Beta, and Gamma variants. The recently developed STANDARD M10 has a shorter turnaround time and random-access detection on automated devices, thereby facilitating efficient testing in emergency settings.

Quantitative Analysis of Anti-N and Anti-S Antibody Titers of SARS-CoV-2 Infection after the Third Dose of COVID-19 Vaccination.

We quantitatively analyzed SARS-CoV-2 antibody levels in patients after two doses of the ChAdOx1 nCoV-19 vaccine and the third BNT162b2 booster. We obtained 255 serum samples from 149 healthcare workers 1 and 4 months after the third dose. Of the 149 participants, 58 (38.9%) experienced COVID-19 infection during the 4-month study period, with infection occurring 7-62 days before the second blood draw. Total antibody titers against the anti-spike (anti-S) and anti-nucleocapsid (anti-N) proteins of SARS-CoV-2 were measured using Elecsys Anti-SARS-CoV-2 S and Elecsys Anti-SARS-CoV-2 assays (Roche), respectively. The median anti-S antibody titer in the non-infected groups at 4 months after the third dose was significantly decreased compared to that at 1 month after the third dose (from 17,777 to 3673 U/mL, p < 0.001). The infected group showed higher median anti-S antibody titers at 4 months (19,539 U/mL) than the non-infected group (3673 U/mL). The median anti-N antibody titer in the infected group at 4 months after the third dose was a 5.07 cut-off index (79.3% positivity). Anti-N antibody titers in the infected group were correlated with the number of days after SARS-CoV-2 infection. These data provide useful information for determining quarantine strategies and fourth vaccination requirements.

Clinical Performance of Rapid and Point-of-Care Antigen Tests for SARS-CoV-2 Variants of Concern: A Living Systematic Review and Meta-Analysis.

Rapid antigen tests (RATs) for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are widely used in the Coronavirus disease 2019 (COVID-19) pandemic caused by diverse variants. Information on the real-world performance of RATs for variants is urgently needed for decision makers. Systematic searches of the available literature and updates were conducted in PubMed, Ovid-MEDLINE, Ovid-EMBASE, CENTRAL, and KMBASE for articles evaluating the accuracy of instrument-free RATs for variants up until 14 March 2022. A bivariate random effects model was utilized to calculate pooled diagnostic values in comparison with real-time reverse transcription-polymerase chain reaction as the reference test. A total of 7562 samples from six studies were available for the meta-analysis. The overall pooled sensitivity and specificity of RATs for variants were 69.7% (95% confidence interval [CI] = 62.5% to 76.1%) and 100.0% (95% CI = 98.8% to 100.0%), respectively. When an additional 2179 samples from seven studies reporting sensitivities only were assessed, the pooled sensitivity dropped to 50.0% (95% CI = 44.0% to 55.0%). These findings suggest reassessment and monitoring of the diagnostic utility of RATs for variants, especially for the sensitivity aspect, to facilitate appropriate diagnosis and management of COVID-19 patients.

Humoral and Cellular Responses to BNT162b2 as a Booster Following Two Doses of ChAdOx1 nCov-19 Determined Using Three SARS-CoV-2 Antibody Assays and an Interferon-Gamma Release Assay: A Prospective Longitudinal Study in Healthcare Workers.

Data on humoral and cellular responses to BNT162b2 as a booster dose, following two doses of ChAdOx1 nCov-19 vaccine, have seldom been reported. The aim of this study was to assess the positivity rates of three representative antibody assays targeting total, IgG, and neutralizing antibodies, and an interferon-γ release assay (IGRA), and to determine the longitudinal changes in quantitative antibody titers after each vaccination. A total of 1027 samples were collected from healthcare workers. The number of participants after the booster dose was 153, and they all completed a questionnaire on adverse reactions. All antibody assays showed 100.0% positivity at 1 month after booster vaccination. The median antibody titers of the assays were significantly increased compared with those after the second dose (22.1-fold increase for Roche total antibody, 14.0-fold increase for Abbott IgG, and 1.1-fold increase (97.5% inhibition) for GenScript neutralizing antibody). Cellular responses determined using the IGRA were positive in 92.8% of the participants. Most participants (72.5%) reported mild adverse reactions. Correlations between the three antibody assays and IGRA were weak or negligible, indicating a difference between humoral and cellular responses. Overall, our study provides information about booster vaccine strategies and laboratory settings, which could subsequently contribute to the control of the spread of coronavirus disease 2019.

Impact of COVID-19 on Antimicrobial Consumption and Spread of Multidrug-Resistance in Bacterial Infections.

The spread of COVID-19 pandemic may have affected antibiotic consumption patterns and the prevalence of colonized or infected by multidrug-resistant (MDR) bacteria. We investigated the differences in the consumption of antibiotics easily prone to resistance and the prevalence of MDR bacteria during the COVID-19 pandemic (March 2020 to September 2021) compared to in the pre-pandemic period (March 2018 to September 2019). Data on usage of antibiotics and infections caused by methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), carbapenem-resistant Enterobacteriaceae (CRE), carbapenem-resistant Acinetobacter baumannii (CRAB), and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were obtained from hospitalized patients in four university hospitals. The consumption of penicillin with ß-lactamase inhibitors (3.4% in ward, 5.8% in intensive care unit (ICU)), and carbapenems (25.9% in ward, 12.1% in ICU) increased during the pandemic period. The prevalence of MRSA (4.7%), VRE (49.0%), CRE (22.4%), and CRPA (20.1%) isolated in clinical samples from the ward and VRE (26.7%) and CRE (36.4%) isolated in clinical samples from the ICU were significantly increased, respectively. Meanwhile, only the prevalence of CRE (38.7%) isolated in surveillance samples from the ward increased. The COVID-19 pandemic is associated with increased consumption of antibiotics and has influenced the prevalence of infections caused by MDR isolates.

Prevalence and Clinical Impact of Coinfection in Patients with Coronavirus Disease 2019 in Korea.

Coinfection rates with other pathogens in coronavirus disease 2019 (COVID-19) varied during the pandemic. We assessed the latest prevalence of coinfection with viruses, bacteria, and fungi in COVID-19 patients for more than one year and its impact on mortality. A total of 436 samples were collected between August 2020 and October 2021. Multiplex real-time PCR, culture, and antimicrobial susceptibility testing were performed to detect pathogens. The coinfection rate of respiratory viruses in COVID-19 patients was 1.4%. Meanwhile, the rates of bacteria and fungi were 52.6% and 10.5% in hospitalized COVID-19 patients, respectively. Respiratory syncytial virus, rhinovirus, Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans were the most commonly detected pathogens. Ninety percent of isolated A. baumannii was non-susceptible to carbapenem. Based on a multivariate analysis, coinfection (odds ratio [OR] = 6.095), older age (OR = 1.089), and elevated lactate dehydrogenase (OR = 1.006) were risk factors for mortality as a critical outcome. In particular, coinfection with bacteria (OR = 11.250), resistant pathogens (OR = 11.667), and infection with multiple pathogens (OR = 10.667) were significantly related to death. Screening and monitoring of coinfection in COVID-19 patients, especially for hospitalized patients during the pandemic, are beneficial for better management and survival.

The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.

Seven-Month Analysis of Five SARS-CoV-2 Antibody Assay Results after ChAdOx1 nCoV-19 Vaccination: Significant Decrease in SARS-CoV-2 Antibody Titer.

We investigated the longevity rates of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after a complete ChAdOx1 nCoV-19 vaccination, which are rare and important to estimate their efficacy and establish a vaccination strategy. We assessed the positivity rates and changes of titers before (T0) and at one month (T1), four months (T2), and seven months (T3) after a ChAdOx1 nCoV-19 vaccination using five SARS-CoV-2 antibody assays. A total of 874 serum samples were obtained from 228 (T0 and T1), 218 (T2), and 200 (T3) healthcare workers. The positive rates for all five assays were 0.0-0.9% at T0, 66.2-92.5% at T1, 98.2-100.0% at T2, and 66.0-100.0% at T3. The positive rates at T3 were decreased compared to those at T2. The median antibody titers of all the assays at T3 were significantly decreased compared to those at T2 (860.5 to 232.0 U/mL for Roche total, 1041.5 to 325.5 AU/mL for Abbott IgG, 10.9 to 2.3 index for Siemens IgG, 99.5% to 94.7% for SD Biosensor V1, and 88.5% to 38.2% for GenScript). A third-dose scheme can be considered based on our data generated from five representative assays. Our findings contribute insights into SARS-CoV-2 antibody assays and appropriate vaccination strategies.

Comparison of the Results of Five SARS-CoV-2 Antibody Assays before and after the First and Second ChAdOx1 nCoV-19 Vaccinations among Health Care Workers: a Prospective Multicenter Study.

Reliable results for serological positivity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody after the second dose of AstraZeneca (AZ) vaccination are important to estimate the real efficacy of vaccination. We evaluated positivity rates and changes in semiquantitative antibody titers before and after the first and second ChAdOx1 nCoV-19 vaccinations using five SARS-CoV-2 antibody assays, including two surrogate virus neutralization tests. A total of 674 serum samples were obtained from 228 participants during three blood sampling periods. A questionnaire on symptoms, severity, and adverse reaction duration was completed by participants after the second vaccination. The overall positive rates for all assays were 0.0 to 0.9% before vaccination, 66.2 to 92.5% after the first vaccination, and 98.2 to 100.0% after the second vaccination. Median antibody titers in five assays after the second dose of vaccination were increased compared to those after the first dose (106.4-fold increase for Roche total antibody, 3.6-fold for Abbott IgG, 3.6-fold for Siemens, 1.2-fold for SD Biosensor V1 neutralizing antibody, and 2.2-fold for GenScript neutralizing antibody). Adverse reactions were reduced after the second dose in 89.9% of participants compared to after the first dose. Overall, the second vaccination led to almost 100% positivity rates based on these SARS-CoV-2 antibody assays. The results should be interpreted with caution, considering the characteristics of the applied assays. Our findings could inform decisions regarding vaccination and the use of immunoassays, thus contributing to SARS-CoV-2 pandemic control.

Comparing Results of Five SARS-CoV-2 Antibody Assays Before and After the First Dose of ChAdOx1 nCoV-19 Vaccine among Health Care Workers.

Reliable results regarding serologic positivity for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody before and after AstraZeneca (AZ) vaccination are essential for estimating the efficacy of vaccination. We assessed positivity rates and associated factors using five SARS-CoV-2 antibody assays. A total of 228 paired serum samples (456 samples) were obtained from 228 participants. After baseline sampling, the second sampling was conducted between 11 and 28 days after the first dose of the AZ vaccine. Sera were tested using five SARS-CoV-2 antibody assays, including two surrogate virus neutralization tests. A questionnaire on the symptoms, severity, and duration of adverse reactions was completed by all participants. The overall positivity rates for SARS-CoV-2 antibody were 84.6% for the Roche assay, 92.5% for the Abbott assay, 75.4% for the Siemens assay, 90.7% for the SD Biosensor assay, and 66.2% for the GenScript assay after the first dose of the AZ vaccine. The positivity rates and antibody titers of sera obtained between 21 and 28 days were significantly higher than those obtained between 11 and 20 days in all five assays. More-severe adverse reactions and longer durations of adverse reactions were related to higher SARS-CoV-2 antibody levels. The agreements and correlations among the assays applied were substantial (к, 0.73 to 0.95) and strong (ρ, 0.83 to 0.91). A single dose of the AZ vaccine led to high positivity rates based on the five assays. Days after vaccination and adverse reactions could help estimate serologic conversion rates. The results should be interpreted cautiously considering the assays and cutoffs applied. Our findings could inform decisions regarding vaccination and laboratory settings and could thus contribute to the control of the spread of SARS-CoV-2 infection.

Comparison of three serological chemiluminescence immunoassays for SARS-CoV-2, and clinical significance of antibody index with disease severity.

BACKGROUND: The clinical significance of the quantitative value of antibodies in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains mostly unidentified. We investigated the dynamics and clinical implications of the SARS-CoV-2 antibody over time using three automated chemiluminescence immunoassays targeting either nucleocapsids or spikes. METHODS: A total of 126 specimens were collected from 23 patients with confirmed and indeterminate COVID-19 identified by molecular tests. SARS-CoV-2 antibody index was measured using SARS-CoV2 IgG reagent from Alinity (Abbott) and Access (Beckman Coulter) and SARS-CoV2 Total (IgG + IgM) from Atellica (Siemens). RESULTS: Three immunoassays showed strong correlations with each other (range of Pearson' s correlation coefficient (r) = 0.700-0.854, P < 0.001). Eleven (8.7%) specimens showed inconsistencies. SARS-CoV-2 IgG showed a statistically significantly higher value in patients with severe disease than that in non-severe disease patients (P < 0.001) and was significantly associated with clinical markers of disease severity. CONCLUSION: The quantitative value of the SARS-CoV-2 IgG antibody measured using automated immunoassays is a significant indicator of clinical severity in patients with COVID-19.