The use of saliva for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sparks debate due to presumed lower sensitivity and lack of standardization. Our aim was to evaluate the performance characteristics of (i) saliva collected by the ORAcollect^TM device as a matrix for SARS-CoV-2 reverse-transcriptase polymerase chain reaction (RT-PCR), and (ii) 2 saliva rapid antigen tests (AgRDT). From 342 ambulatory individuals, both a nasopharyngeal swab and saliva sample via ORAcollect^TM were obtained for a SARS-CoV-2 RT-PCR test. Furthermore, 54 and 123 additionally performed the V-Chek^TM or Whistling^TM saliva AgRDT. In total, 35% of individuals screened positive for SARS-CoV-2 via nasopharyngeal swab. Saliva, as a matrix for the RT-PCR, had a specificity of 96.5% and a negative predictive value (NPV) of 91.3%. Interestingly, 6 out of 8 patients thought to be false positive in saliva re-tested positive by nasopharyngeal sampling after 2 to 9 days. Both V-Chek^TM and Whistling^TM AgRDT had a lack of sensitivity, resulting in an NPV of 66.9 and 67.3%, respectively. Saliva proved to be a sensitive and specific matrix for SARS-CoV-2 detection by the RT-PCR. In this setting, saliva might have an earlier window of detection than the nasopharyngeal swab. By contrast, both AgRDT showed an unacceptably low sensitivity and NPV.

Objectives: Development and implementation of SARS-CoV-2 serologic assays gained momentum. Laboratories keep on investigating the performance of these assays. In this study, we compared three fully automated SARS-CoV-2 antibody assays. Methods: A total of 186 samples from 84 PCR-positive COVID-19 patients and 120 control samples taken before the SARS-CoV-2 pandemic were analyzed using commercial serologic assays from Roche, Siemens and DiaSorin. Time after the positive COVID-19 PCR result and onset of symptoms was retrieved from the medical record. An extended golden standard, using the result of all three assays was defined, judging if antibodies are present or absent in a sample. Diagnostic and screening sensitivity/specificity and positive/negative predictive value were calculated. Results: Diagnostic sensitivity (ability to detect a COVID-19 positive patient) ≥14 days after positive PCR testing was 96.7% (95% CI 88.5–99.6%) for DiaSorin, 93.3% (95% CI 83.8–98.2%) for Roche and 100% (95% CI 94.0–100%) for Siemens. Lower diagnostic sensitivities were observed <14 days after onset of symptoms for all three assay. Diagnostic specificity (ability to detect a COVID-19 negative patient) was 95.0% (95% CI 89.4–98.1%) for DiaSorin, 99.2% (95% CI 95.4–99.9%) for Roche and 100% (95% CI 97.0–100%) for Siemens. The sensitivity/specificity for detecting antibodies (ability of detecting absence (specificity) or presence (sensitivity) of COVID-19 antibodies) was 92.4% (95% CI 86.4–96.3%)/94.9% (95% CI 90.5–97.6%) for DiaSorin, 97.7% (95% CI 93.5–99.5%)/97.1% (95% CI 93.5–99.1%) for Roche and 98.5% (95% CI 94.6–99.8)/97.1 (95% CI 93.5–99.1%) for Siemens. Conclusions: This study revealed acceptable performance for all three assays. An orthogonal testing algorithm using the Siemens and Roche assay achieved the highest positive predictive values for antibody detection in low seroprevalence settings.

Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure. In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases) (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3′ mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile. Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples. Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis.

With the present paper, the Working Group on Cells, Tissues and Organs and other experts of the Superior Health Council of Belgium aimed to provide stakeholders in material of human origin with advice on critical aspects of serological and nucleic acid test (NAT) testing, to improve virological safety of cell- and tissue and organ donation. The current paper focusses on a number of preanalytical variables which can be critical for any medical biology examination: (1) sampling related variables (type of samples, collection of the samples, volume of the sample, choice of specific tubes, identification of tubes), (2) variables related to transport, storage and processing of blood samples (transport, centrifugation and haemolysis, storage before and after centrifugation, use of serum versus plasma), (3) variables related to dilution (haemodilution, pooling of samples), and (4) test dependent variables (available tests and validation). Depending on the type of donor (deceased donor (heart-beating or non-heart beating) versus living donor (allogeneic, related, autologous), and the type of donated human material (cells, tissue or organs) additional factors can play a role: pre- and post-mortem sampling, conditions of sampling (e.g. morgue), haemodilution, possibility of retesting.

Summary: Antigen recognition through the T cell receptor (TCR) αβ heterodimer is one of the primary determinants of the adaptive immune response. Vaccines activate naïve T cells with high specificity to expand and differentiate into memory T cells. However, antigen-specific memory CD4 T cells exist in unexposed antigen-naïve hosts. In this study, we use high-throughput sequencing of memory CD4 TCRβ repertoire and machine learning to show that individuals with preexisting vaccine-reactive memory CD4 T cell clonotypes elicited earlier and higher antibody titers and mounted a more robust CD4 T cell response to hepatitis B vaccine. In addition, integration of TCRβ sequence patterns into a hepatitis B vaccine specific model can predict which individuals will have an early and more vigorous vaccine-elicited immunity. Thus, the presence of preexisting memory T clonotypes has a significant impact on immunity and can be used to predict immune responses to vaccination.