Semi-quantitative assessment of gastrointestinal viruses in stool samples with Seegene Allplex gastrointestinal panel assays: a solution to the interpretation problem of multiple pathogen detection?

PURPOSE

The aim of the study was to determine and evaluate the clinical usefulness of pathogen specific semi-quantitative cut-offs in stool samples with multiple pathogen detections.

METHODS

The PCR (Seegene Allplex Gastrointestinal Virus Assay) data from 4527 positive samples received over 16 months were retrospectively analyzed to investigate the distribution of the Ct values of each individual viral pathogen. By using interquartile ranges for each viral pathogen, pathogen specific semi-quantitative cut-offs were determined.

RESULTS

After a thorough analysis of the Ct values, a well-founded decision to exclude all results with a Ct value higher than 35 was made. This approach made it possible to generate a more nuanced report and to facilitate clinical interpretation in case of mixed infections by linking a lower Ct value of a pathogen to a greater likelihood of being a relevant causative pathogen. Moreover, not reporting viral pathogens with a Ct value higher than 35 led to a significant reduction (p < 0.0001) of reported mixed infections compared to oversimplified qualitative or qualitative reporting.

CONCLUSION

By omitting very high Ct values and reporting semi-quantitatively, value was added to the syndromic reports, leading to an easier to read lab report, especially in mixed infections.

Digital PCR to Measure SARS-CoV-2 RNA, Variants, and Outcomes in Youth

BACKGROUND

The role of SARS-CoV-2 viral load in predicting contagiousness, disease severity, transmissibility, and clinical decision-making continues to be an area of great interest. However, most studies have been in adults and have evaluated SARS-CoV-2 loads using cycle thresholds (Ct) values, which are not standardized preventing consistent interpretation critical to understanding clinical impact and utility. Here, a quantitative SARS-CoV-2 reverse-transcription digital PCR (RT-dPCR) assay normalized to WHO International Units was applied to children at risk of severe disease diagnosed with COVID-19 at St. Jude Children's Research Hospital between March 28, 2020, and January 31, 2022.

METHODS

Demographic and clinical information from children, adolescents, and young adults treated at St. Jude Children's Research Hospital were abstracted from medical records. Respiratory samples underwent SARS-CoV-2 RNA quantitation by RT-dPCR targeting N1 and N2 genes, with sequencing to determine the genetic lineage of infecting virus.

RESULTS

Four hundred and sixty-two patients aged 0-24 years (median 11 years old) were included during the study period. Most patients were infected by the omicron variant (43.72%), followed by ancestral strain (22.29%), delta (13.20%), and alpha (2.16%). Viral load at presentation ranged from 2.49 to 9.14 log10 IU/mL, and higher viral RNA loads were associated with symptoms (OR 1.32; CI 95% 1.16-1.49) and respiratory disease (OR 1.23; CI 95% 1.07-1.41). Viral load did not differ by SARS-CoV-2 variant, vaccination status, age, or baseline diagnosis.

CONCLUSIONS

SARS-CoV-2 RNA loads predict the presence of symptomatic and respiratory diseases. The use of standardized, quantitative methods is feasible, allows for replication, and comparisons across institutions, and has the potential to facilitate consensus quantitative thresholds for risk stratification and treatment.

Viral load: We need a new look at an old problem?

The concept of viral load was introduced in the 1980s to measure the amount of viral genetic material in a person's blood, primarily for human immunodeficiency virus (HIV). It has since become crucial for monitoring HIV infection progression and assessing the efficacy of antiretroviral therapy. However, during the coronavirus disease 2019 pandemic, the term "viral load" became widely popularized, not only for the scientific community but for the general population. Viral load plays a critical role in both clinical patient management and research, providing valuable insights for antiviral treatment strategies, vaccination efforts, and epidemiological control measures. As measuring viral load is so important, why don't researchers discuss the best way to do it? Is it simply acceptable to use raw Ct values? Relying solely on Ct values for viral load estimation can be problematic due to several reasons. First, Ct values can vary between different quantitative polymerase chain reaction assays, platforms, and laboratories, making it difficult to compare data across studies. Second, Ct values do not directly measure the quantity of viral particles in a sample and they can be influenced by various factors such as initial viral load, sample quality, and assay sensitivity. Moreover, variations in viral RNA extraction and reverse-transcription steps can further impact the accuracy of viral load estimation, emphasizing the need for careful interpretation of Ct values in viral load assessment. Interestingly, we did not observe scientific articles addressing different strategies to quantify viral load. The absence of standardized and validated methods impedes the implementation of viral load monitoring in clinical management. The variability in cell quantities within samples and the variation in viral particle numbers within infected cells further challenge accurate viral load measurement and interpretation. To advance the field and improve patient outcomes, there is an urgent need for the development and validation of tailored, standardized methods for precise viral load quantification.

SARS-CoV-2 RT-qPCR Ct values in saliva and nasopharyngeal swab samples for disease severity prediction

Background

Comparison of clinical value of RT-qPCR-based SARS-CoV-2 tests performed on saliva samples (SSs) and nasopharyngeal swab samples (NPSs) for prediction of the COVID-19 disease severity.

Methods

Three paired SSs and NPSs collected every 3 days from 100 hospitalised COVID-19 patients during 2020 Jul-2021 Jan were tested by RT-qPCR for the original SARS-CoV-2 virus and compared to 150 healthy controls. Cases were divided into mild+moderate (Cohort I, N = 47) and severe disease (Cohort II, N = 53) cohorts and compared.

Results

SARS-CoV-2 was detected in 65% (91/140) vs. 53% (82/156) of NPSs and 49% (68/139) vs. 48% (75/157) of SSs collected from Cohort I and II, respectively, resulting in the total respective detection rates of 58% (173/296) vs. 48% (143/296) (P = 0.017). Ct values of SSs were lower than those of NPSs (mean Ct = 28.01 vs. 30.07, P = 0.002). Although Ct values of the first SSs were significantly lower in Cohort I than in Cohort II (P = 0.04), it became negative earlier (mean 11.7 vs. 14.8 days, P = 0.005). Multivariate Cox proportional hazards regression analysis showed that Ct value ≤30 from SSs was the independent predictor for severe COVID-19 (HR = 10.06, 95% CI: 1.84-55.14, P = 0.008).

Conclusion

Salivary RT-qPCR testing is suitable for SARS-CoV-2 infection control, while simple measurement of Ct values can assist in prediction of COVID-19 severity.

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.

Comparison of FTD SARS-CoV-2 Assay and RealStar RT-PCR kit 1.0 for the detection of SARS-CoV-2

The aim of the study was to evaluate the clinical performance of FTD SARS-CoV-2 compared to the RealStar RT-PCR kit 1.0. The analysis of 100 nasopharyngeal swabs showed an overall agreement of 88 %. The positive percentage agreement was 85.6 % and the negative percentage agreement was 91 %. In conclusion we observed a substantial agreement among the two methods, with discrepancies mainly observed in specimens with relatively low amount of viral RNA.

Viral Dynamics and Real-Time RT-PCR Ct Values Correlation with Disease Severity in COVID-19

Real-time RT-PCR is considered the gold standard confirmatory test for coronavirus disease 2019 (COVID-19). However, many scientists disagree, and it is essential to understand that several factors and variables can cause a false-negative test. In this context, cycle threshold (Ct) values are being utilized to diagnose or predict SARS-CoV-2 infection. This practice has a significant clinical utility as Ct values can be correlated with the viral load. In addition, Ct values have a strong correlation with multiple haematological and biochemical markers. However, it is essential to consider that Ct values might be affected by pre-analytic, analytic, and post-analytical variables such as collection technique, specimen type, sampling time, viral kinetics, transport and storage conditions, nucleic acid extraction, viral RNA load, primer designing, real-time PCR efficiency, and Ct value determination method. Therefore, understanding the interpretation of Ct values and other influential factors could play a crucial role in interpreting viral load and disease severity. In several clinical studies consisting of small or large sample sizes, several discrepancies exist regarding a significant positive correlation between the Ct value and disease severity in COVID-19. In this context, a revised review of the literature has been conducted to fill the knowledge gaps regarding the correlations between Ct values and severity/fatality rates of patients with COVID-19. Various databases such as PubMed, Science Direct, Medline, Scopus, and Google Scholar were searched up to April 2021 by using keywords including "RT-PCR or viral load", "SARS-CoV-2 and RT-PCR", "Ct value and viral load", "Ct value or COVID-19". Research articles were extracted and selected independently by the authors and included in the present review based on their relevance to the study. The current narrative review explores the correlation of Ct values with mortality, disease progression, severity, and infectivity. We also discuss the factors that can affect these values, such as collection technique, type of swab, sampling method, etc.

S-Variant SARS-CoV-2 Lineage B1.1.7 Is Associated With Significantly Higher Viral Load in Samples Tested by TaqPath Polymerase Chain Reaction

A SARS-CoV-2 variant B1.1.7 containing a mutation Δ69/70 has spread rapidly in the UK and shows an identifiable profile in ThermoFisher TaqPath RTqPCR (S-gene target failure; SGTF). We analysed recent test data for trends and significance. Linked Ct values for respiratory samples showed that a low Ct for ORF1ab and N were clearly associated with SGTF. Significantly more SGTF samples had higher inferred viral loads between 1x10 ⁷ and 1x10 ⁸. Our conclusion is that patients whose samples exhibit the SGTF profile are more likely to have high viral loads, which may explain higher infectivity and rapidity of spread.

CRISPR/Cas12a-Based Versatile Method for Checking Quantitative Polymerase Chain Reaction Samples with Cycles of Threshold Values in the Gray Zone

Quantitative polymerase chain reaction (qPCR) is widely applied in foodborne pathogen detection and diagnosis. According to the cycles of threshold (Ct) values of qPCR testing, samples are judged as positive or negative. However, samples with Ct values in the gray zone are classified as "possibly positive" and required to be tested again. Repetitive qPCR may not eliminate the uncertain results but increase the workload of detection. CRISPR/Cas12a can specifically recognize the nucleic acid of the nM level and then indiscriminately slash the single-strand DNA with multiple turnovers. In this way, the detection signals can be greatly amplified. Here, we propose a CRISPR-based checking method to solve gray zone problems. After qPCR testing, the screening gray zone samples can be successfully checked by the CRISPR/Cas12a method. Furthermore, to conduct CRISPR reaction assay more conveniently and prevent possible aerosol contamination in the operational process, a gray zone checking cassette is designed. African swine fever virus (ASFV) is selected as an example to demonstrate the feasibility of the CRISPR-based checking method. Of 28 real swine blood samples, 6 ASFV qPCR gray zone samples are successfully checked. The CRISPR-based checking method provides a novel solution to eliminate gray zone sample problems with no additional effects on the PCR, which is operable and applicable in practical detection. The entire process can be completed within 10-15 min. This method will be a good supplementary and assistance for qPCR-based detection, especially in the diagnosis of diseases such as COVID-19.

Comparative evaluation of four SARS-CoV-2 antigen tests in hospitalized patients

OBJECTIVES

Rapid identification of infected subjects is a cornerstone for controlling a pandemic like the current one with the SARS-CoV-2. Easy to handle antigen tests can provide timely results, which is of particular importance in a primary care setting. However, concerns exist regarding their sensitivity, which led us to evaluate four commercially available tests in patients hospitalized for COVID-19.

METHODS

We analyzed in parallel nasopharyngeal/oropharyngeal swabs from 154 consecutive patients admitted to our department with moderate to severe COVID-19, using quantitative RT-PCR (Cobas, Roche) and up to four antigen tests from different distributors. Antigen test results were linked to Ct (cycle threshold) values as markers for patients' infectivity.

RESULTS

We found that two out of four antigen tests correctly identified subjects with high viral loads (Ct≤25), and three out of four tests detected more than 80% of subjects with a Ct≤30, which is considered the threshold for infectivity. However, one test investigated had a poor clinical performance. When investigating subjects with Ct values >30, we found that the antigen test was still positive in up to 45% of those cases.

CONCLUSION

Most antigen tests had a sufficient sensitivity to identify symptomatic subjects infected with SARS-CoV-2 and with transmissible infection. On the other hand, antigen testing may not be suitable to identify loss of infectivity in COVID-19 subjects during follow-up. Newly introduced antigen tests need to be validated in a clinical or primary care setting to define their clinical usefulness.

Misinterpretation of viral load in COVID-19 clinical outcomes

Knowledge of viral load is essential to formulate strategies for antiviral treatment, vaccination, and epidemiological control of COVID-19. Moreover, identification of patients with high viral loads can also be useful to understand risk factors such as age, comorbidities, severity of symptoms and hypoxia, to decide on the need for hospitalization. Several ongoing studies are analyzing viral load in different types of samples and evaluating its relationship with clinical outcomes and viral transmission pathways. However, in a great number of emerging studies, cycle threshold (Ct) values alone are often used as viral load indicators, which may be a mistake. In this study, we compared tracheal aspirate with nasopharyngeal swab samples obtained from critically ill COVID-19 patients and here we report how the raw Ct can lead to misinterpretation of results. Furthermore, based on analysis of nasopharyngeal swab samples we propose a method to reduce evaluation errors that could occur from using raw Ct data. Based on these findings, we show the impact that normalization of Ct values has on interpretation of SARS-CoV-2 viral load from different biological samples.

Assessing the burden of viral co-infections in acute gastroenteritis in children: An eleven-year-long investigation

BACKGROUND

Acute gastroenteritis is an important cause of childhood morbidity and mortality worldwide. A number of pathogens are responsible for human acute gastroenteritis. The recent introduction of syndromic assays for the diagnosis of enteric infections, including a wide panel of enteric pathogens, has unveiled the frequency of mixed infections. This study was carried out to assess the burden of viral co-infections and the genetic diversity of the viruses detected in children hospitalized with acute gastroenteritis in Italy.

METHODS

A total of 4161 stool samples collected from diarrheic children over 11 years, from January 2008 to December 2018, were investigated for the presence of four enteric viruses, i.e. group A rotavirus, norovirus, astrovirus and adenovirus. The samples were initially screened by either molecular or immunochromatographic assays and subsequently confirmed by Real-time PCR and sequence analyses.

RESULTS

At least one viral agent was detected in 48.6 %of specimens. Rotavirus was the most prevalent virus (24.7 %) followed by norovirus (19.6 %), adenovirus (5.3 %) and astrovirus (3%). Co-infections were detected in 8.3 % of virus-positive patients, with common viral combination being rotavirus with norovirus (70.6 % of co-infections) or with astrovirus (9.6 %). A variety of viral genotypes was detected in co-infections and in single infections. Using Real-time PCR cycle thresholds as a proxy measure of fecal viral load, rotavirus was generally detected at higher levels in co-infected patients.

CONCLUSIONS

Combining and deciphering measurable indicators of viral load and epidemiological information could be useful for an accurate interpretation of viral co-infections.