Evaluation of the analytical and clinical performance of two RT-PCR based point-of-care tests; Cepheid Xpert® Xpress CoV-2/Flu/RSV plus and SD BioSensor STANDARD™ M10 Flu/RSV/SARS-CoV-2.

BACKGROUND: Rapid and accurate detection of viral respiratory infections is important for infection control measures. This study compares the analytical and clinical performance of the Xpert® Xpress CoV-2/Flu/RSV plus test ("Xpert", Cepheid) and the STANDARD™ M10 Flu/RSV/SARS-CoV-2 test ("M10", SD Biosensor). Both tests are quadruplex RT-PCR assays for rapid diagnosis of SARS-CoV-2, influenza A/B and RSV. STUDY DESIGN: Analytical sensitivities were determined by limit of detection for SARS-CoV-2, influenza A, influenza B and RSV, respectively. Additionally, the clinical performance of the Xpert and the M10 tests was evaluated against standard-of-care RT-PCR by testing of 492 clinical specimens. RESULTS: The analytical sensitivities for Xpert versus M10 test was 10, 50, 50 and 300 versus 300, 200, 800 and 1500 copies/mL for SARS-CoV-2, influenza A, influenza B and RSV, respectively. Clinical sensitivity for the Xpert test was superior across all four pathogens compared to the M10 test. Xpert showed clinical sensitivity of 100 % in all Ct-ranges for all four pathogens whereas M10 showed clinical sensitivity of 100 % in the 25-30 Ct-range, 84-100 % in the 30-35 Ct-range and 47-67 % in the >35 Ct-range across the four pathogens. Translating into real-life clinical sensitivity, the Xpert would detect 100 % of all four pathogens, whereas M10 would detect 92.1, 92.4, 84.8 and 94.7 % for SARS-CoV-2, influenza A, influenza B and RSV. CONCLUSION: This study demonstrates improved analytical and clinical performance of Xpert Xpress CoV-2/Flu/RSV plus compared to STANDARD M10 Flu/RSV/SARS-CoV-2, which is important for ensuring accuracy of diagnosis at all stages of a respiratory infection.

Emergence of circulating influenza A H3N2 viruses with genetic drift in the matrix gene: be alert of false-negative test results.

In March 2022, we observed samples with a negative fluorescent signal (60.5%, n = 43) for the influenza A matrix gene and a stronger positive signal for subtype A(H3N2). Forty-three samples were positive in InfA (H3N2) (mean Cq 30.9, range 23.9-35.1), and 26 of the 43 samples were negative in InfA matrix (mean Cq 28.0, range 23.2-30.6). Our multiplex test is a laboratory-developed four-target, four-color influenza A reverse-transcription PCR assay targeting the matrix gene, subtypes A(H3N2) and A(H1N1)pdm09. Several samples were negative when retested on commercial influenza Point-of-Care assays. As the matrix gene is a stand-alone target in most commercial diagnostic assays, we caution against false-negative subtype A test results.

A late sharp increase in influenza detections and low interim vaccine effectiveness against the circulating A(H3N2) strain, Denmark, 2021/22 influenza season up to 25 March 2022.

We estimated interim influenza A vaccine effectiveness (VE) following a late sharp rise in cases during an influenza A(H3N2)-dominated 2021/22 season, after lifting COVID-19 restrictions. In children aged 2-6 years offered a live attenuated influenza vaccine, adjusted VE was 62.7% (95% CI: 10.9-84.4) in hospitalised and 64.2% (95% CI: 50.5-74.1) in non-hospitalised children. In non-hospitalised patients aged 7-44 years, VE was 24.8% (95% CI: 12.8-35.2); VE was non-significant in remaining age groups and hospital/non-hospital settings.

Holo-Omics: Integrated Host-Microbiota Multi-omics for Basic and Applied Biological Research.

From ontogenesis to homeostasis, the phenotypes of complex organisms are shaped by the bidirectional interactions between the host organisms and their associated microbiota. Current technology can reveal many such interactions by combining multi-omic data from both hosts and microbes. However, exploring the full extent of these interactions requires careful consideration of study design for the efficient generation and optimal integration of data derived from (meta)genomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics. In this perspective, we introduce the holo-omic approach that incorporates multi-omic data from both host and microbiota domains to untangle the interplay between the two. We revisit the recent literature on biomolecular host-microbe interactions and discuss the implementation and current limitations of the holo-omic approach. We anticipate that the application of this approach can contribute to opening new research avenues and discoveries in biomedicine, biotechnology, agricultural and aquacultural sciences, nature conservation, as well as basic ecological and evolutionary research.