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.
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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).
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Natural Deep Eutectic Solvents (NADES) represent a green chemistry alternative to utilization of common hazardous organic solvents. They were introduced by Abbott et al. [1], and were found to have a wide range of compositions and favorable properties. NADES are typically obtained by mixing hydrogen-bond acceptors (HBA), with hydrogen bond donors (HBD), leading to a significant depression of the melting point. The availability of components, simple preparation, biodegradability, safety, re usability and low cost are the significant advantages that call for research on their analytical applications. Three methods are most commonly used for preparing NADES: a) heating and stirring: the mixture until a clear liquid is formed; b) evaporating solvent from components solution with a rotatory evaporator; c) freeze drying of aqueous solutions.The common solvents for the extraction of anthocyanins are acidified mixtures of water with ethanol, methanol, or acetone. The anthocyanins extracts are susceptible to degradation due to high temperature, and the solvent properties (e.g. high pH) and the whole process can often be time-consuming. Extraction of anthocyanins from red cabbage by four NADES was investigated. It was demonstrated that NADES have comparable extraction efficiencies with conventional method with 0.1 M water solution of HCl. This indicates a possibility of utilization the Green chemistry extraction processes as a promising new green-extraction technology with low cost efficiency and environment friendly technology for production of safe food additives.
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Oil extraction from Andean lupin beans (Lupinus mutabilis SWEET) via supercritical carbon dioxide (scCO2) was studied on both lab scale and pilot scale. On the lab scale, the effect of pressure, solvent-to-feed ratio (S/F), sample particle size and temperature on oil yield were evaluated. The oil quality (fatty acid [FA] composition and tocopherol content) were investigated. Five-hour scCO2 extraction yielded about 86% oil of Soxhlet extraction (using hexane as solvent). The fraction of unsaturated FA rose with extraction pressure at specific time. High tocopherol contents were detected in oils extracted at low pressure. An increase in temperature was unfavorable to oil and tocopherol yield, thereby confirming the validity for preserving oil extract quality under a mild scCO2 extraction condition. Oil quality and yield did not have identical optimum settings, opening up possibilities for producing different qualities of oils. Pilot-scale extraction offered comparable oil yield to lab-scale extraction at similar S/F ratio. Economic evaluation showed that it is promising to implement industrial scale scCO2 process for lupin oil extraction. It was predicted that, at a specific industrial scale of extraction (2 × 1000 L, 550 bar, 40°C and S/F of 24), the manufacturing cost of oils got close to actual commercial production cost.
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The increasing interest in Aronia melanocarpa berries for their antioxidant properties sets the necessity to define sustainable strategies to valorize the by-products. This study investigates the use of supercritical carbon dioxide (scCO2) extraction, with and without ethanol as a co-solvent, for recovering lipophilic and phenolic compounds from aronia pomace. Extractions were performed at 20, 30, and 40 MPa and temperatures of 50, 70, and 90 °C. A yield of about 2.7 g per 100 g of dried pomace was obtained at 40 MPa and 90 °C. However, the highest total phenolic content of about 165 mg of gallic acid equivalent per 100 g of dried pomace was achieved with 5 % ethanol co-solvent at 50 °C and 30 MPa. The lipid extracts were rich in linoleic acid and the wax portion increased under low-density scCO2 conditions. A feedforward neural network was developed to model extraction kinetics and predict yield as a function of temperature, pressure, and time, demonstrating high predictive accuracy. These findings highlight scCO2 extraction as a viable route for the efficient and selective recovery of valuable bioactives from aronia pomace, contributing to a circular bioeconomy.
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The Andean lupin (Lupinus mutabilis) is one of the lost crops of Incas and has been grown in South America and as a food crop for thousands of years. The seeds are the main source of commercial value regarding the high content of oil (about 20%), protein (about 43%) and carbohydrates (about 33%). A European Union H2020 project, LIBBIO, aims to develop and optimize the breeding and cropping of the Andean lupin in the Europe, and to process the lupin seeds for new and high-value products for consumers and for incorporation into otherproducts. This study works at optimizing the oil extraction from the lupin seeds using supercritical carbon dioxide (scCO2), which has been tested for lupin oil extraction and is advantageous over organic extractants due to the mild operating temperature, costeffectiveness, nontoxicity, and easy post-separation.In the study designed by response surface methodology, the operating pressure,temperature, scCO2 flowrate, and sample mesh size, were investigated on their effect on the oil extraction efficiency. The pressure, scCO2 flowrate and mesh size were found to affect the extraction efficiency significantly. The higher the pressure and the smaller the mesh, the more oil was extracted over a specific period. Optimally about 85% of the oil was extracted by scCO2 compared with conventional Soxhlet extraction using hexane as the extractant. Oleicacid (46%) and Linoleic acid (32%) are the two main fatty acids in the extracted oil. About 80% of the fatty acids are unsaturated. The stearic acid is one of the main saturated fatty acids, which has relatively positive effects on human health to others. The pressure was found to significantly affect the fractions of the saturated and unsaturated fatty acids. The content of tocopherols in the extracted oil ranged from 1 to 20 mg/100g oil, which is comparable withliterature value.
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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.
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Using either freshly pulped or preserved seaweed biomass for the extraction of protein can have a great effect on the amount of protein that can be extracted. In this study, the effect of four preservation techniques (frozen, freeze-dried, and air-dried at 40 and 70 °C) on the protein extractability, measured as Kjeldahl nitrogen, of four seaweed species, Chondrus crispus (Rhodophyceae), Ascophyllum nodosum, Saccharina latissima (both Phaeophyceae) and Ulva lactuca (Chlorophyceae), was tested and compared with extracting freshly pulped biomass. The effect of preservation is species dependent: in all four seaweed species, a differenttreatment resulted in the highest protein extractability. The pellet (i.e., the non-dissolved biomass after extraction) was also analyzed as in most cases the largest part of the initial protein ended up in the pellet and not in the supernatant. Of the four species tested, freeze-dried A. nodosum yielded the highest overall protein extractability of 59.6% with a significantly increased protein content compared with the sample before extraction. For C. crispus extracting biomass air-dried at 40 °C gave the best results with a protein extractability of 50.4%. Preservation had little effect on the protein extraction for S. latissima; only air-drying at 70 °C decreased the yield significantly. Over 70% of the initial protein ended up in the pellet for all U. lactuca extractions while increasing the protein content significantly. Extracting freshly pulped U. lactuca resulted in a 78% increase in protein content in the pellet while still containing 84.5% of the total initial total protein. These results show the importance of the right choice when selecting a preservation method and seaweed species for protein extraction. Besides the extracted protein fraction, the remainingpellet also has the potential as a source with an increased protein content.
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
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