RT-PCR-based assessment of the SD Bioline Rota/Adeno Antigen-based test in infants with and without diarrhea.

BACKGROUND: Rotavirus A (RVA) infections remain a major cause of severe acute diarrhea affecting children worldwide. To date, rapid diagnostic tests (RDT) are widely used to detect RVA. However, paediatricians question whether the RDT can still detect the virus accurately. Therefore, this study aimed to evaluate the performance of the rapid rotavirus test in comparison to the one-step RT-qPCR method. METHODS: A cross-sectional study was conducted in Lambaréné, Gabon, from April 2018 to November 2019. Stool samples were collected from children under 5 years of age with diarrhoea or a history of diarrhoea within the last 24 h, and from asymptomatic children from the same communities. All stool samples were processed and analysed using the SD BIOLINE Rota/Adeno Ag RDT against a quantitative reverse transcription PCR (RT-qPCR), which is considered the gold standard. RESULTS: For a total of 218 collected stool samples, the overall sensitivity of the RDT was 46.46% (confidence interval (CI) 36.38-56.77), with a specificity of 96.64% (CI 91.62-99.08) compared to one-step RT-qPCR. After confirming the presence or absence of RVA gastroenteritis, the RDT showed suitable results in detecting rotavirus A-associated disease, with a 91% concordance with the RT-qPCR. Furthermore, the performance of this test varied when correlated with seasonality, symptoms, and rotavirus genotype. CONCLUSION: This RDT showed high sensitivity and was suitable for the detection of RVA in patients with RVA gastroenteritis, although some asymptomatic RVA shedding was missed by RT-qPCR. It could be a useful diagnostic tool, especially in low-income countries.

Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.

INTRODUCTION: Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microscopists. An automated digital microscope with artificial intelligence (Schistoscope), offers a potential solution. This field study aimed to validate the diagnostic performance of the Schistoscope for detecting and quantifying Schistosoma haematobium eggs in urine compared to conventional microscopy and to a composite reference standard (CRS) consisting of real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of schistosome circulating anodic antigen (CAA). METHODS: Based on a non-inferiority concept, the Schistoscope was evaluated in two parts: study A, consisting of 339 freshly collected urine samples and study B, consisting of 798 fresh urine samples that were also banked as slides for analysis with the Schistoscope. In both studies, the Schistoscope, conventional microscopy, real-time PCR and UCP-LF CAA were performed and samples with all the diagnostic test results were included in the analysis. All diagnostic procedures were performed in a laboratory located in a rural area of Gabon, endemic for S. haematobium. RESULTS: In study A and B, the Schistoscope demonstrated a sensitivity of 83.1% and 96.3% compared to conventional microscopy, and 62.9% and 78.0% compared to the CRS. The sensitivity of conventional microscopy in study A and B compared to the CRS was 61.9% and 75.2%, respectively, comparable to the Schistoscope. The specificity of the Schistoscope in study A (78.8%) was significantly lower than that of conventional microscopy (96.4%) based on the CRS but comparable in study B (90.9% and 98.0%, respectively). CONCLUSION: Overall, the performance of the Schistoscope was non-inferior to conventional microscopy with a comparable sensitivity, although the specificity varied. The Schistoscope shows promising diagnostic accuracy, particularly for samples with moderate to higher infection intensities as well as for banked sample slides, highlighting the potential for retrospective analysis in resource-limited settings. TRIAL REGISTRATION: NCT04505046 ClinicalTrials.gov.