Standard SARS-CoV-2 testing protocols using nasopharyngeal/throat (NP/T) swabs are invasive and require trained medical staff for reliable sampling. In addition, it has been shown that PCR is more sensitive as compared to antigen-based tests. Here we describe the analytical and clinical evaluation of our in-house RNA extraction-free saliva-based molecular assay for the detection of SARS-CoV-2. Analytical sensitivity of the test was equal to the sensitivity obtained in other Dutch diagnostic laboratories that process NP/T swabs. In this study, 955 individuals participated and provided NP/T swabs for routine molecular analysis (with RNA extraction) and saliva for comparison. Our RT-qPCR resulted in a sensitivity of 82,86% and a specificity of 98,94% compared to the gold standard. A false-negative ratio of 1,9% was found. The SARS-CoV-2 detection workflow described here enables easy, economical, and reliable saliva processing, useful for repeated testing of individuals.
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A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite satisfactory results, this measure does not take (semantic) relations between words into account. In this paper we study some alternative relevance measures that do use relations between words. They are computed by defining co-occurrence distributions for words and comparing these distributions with the document and the corpus distribution. We then evaluate keyword extraction algorithms defined by selecting different relevance measures. For two corpora of abstracts with manually assigned keywords, we compare manually extracted keywords with different automatically extracted ones. The results show that using word co-occurrence information can improve precision and recall over tf.idf.
BACKGROUND: Sour cherry (Prunus cerasus L.) stones are the major byproduct of the cherry industry and the efficient management of this biowaste can lead to achieving the food processing sustainability aimed at by the modern food industry. Despite its significant content of lipids, the valorization of cherry stone waste as feedstock for lipid extraction appears to be limited due to the high moisture content. This study explores the primary factors that affect the yield of lipid extraction using Soxhlet, Randall and supercritical carbon dioxide (scCO2) extraction methods, with a particular emphasis on yield optimization for green extraction technologies (scCO2). RESULTS: The investigation revealed an increased lipid extraction yield for scCO2 from 7.4 for dry crushed stones to 20.6 g per 100 g dry weight when the cherry kernels are separated. The high initial moisture content affected all three extraction methods, but mostly impacted the scCO2 extraction, resulting in the co-extraction of an aqueous phase. Lipid and aqueous yield could be manipulated by time, temperature and pressure. However, no observable influence on the composition of fatty acid methyl esters was detected. CONCLUSION: Numerous approaches are shown to enhance the lipid yield from cherry stone waste, depending on the desired outcome. When dealing with wet samples, Randall extraction proves to be the most effective method. On the other hand, scCO2 extraction presents distinct advantages, such as the extraction of food-grade lipids and the co-extraction of a unique aqueous phase, which comes at the expense of a reduced lipid yield. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
