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COVID-19 caused by SARS-CoV-2 is a serious and potentially deadly disease with an incidence rate of 36.1 per 100 000 population in the EU/EEA as of 25 March [Coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK – seventh update Rapid Risk Assessment, ECDC, 15.03.2020]. More than 80 000 new cases are reported worldwide each day [Situation update worldwide, as of 19 April 2020, ECDC, 19.04.2020]. Early diagnosis and isolation of the infected people will play an important role in stopping the further escalation of this pandemic. At the moment, application of nasopharyngeal swabs is the most common method for specimen collection. However, this method requires qualified personnel to collect the specimen, thus exposing the medical stuff to a higher risk of infection, and shortages of swabs are frequently reported. Due to these factors, the nasopharyngeal swab collection method is causing financial and logistic burden on healthcare systems. Additionally, nasopharyngeal specimen collection method causes serious discomfort to the patients. Besides, there are several contraindications, such as coagulopathy or anticoagulant therapy and significant nasal septum deviation. Clearly, there is a need for a simple, less invasive method that also reduces the risk to healthcare personnel. One candidate for specimen collection is saliva. Several scientific papers have been published on the possible use of saliva as a specimen for COVID-19 diagnosis. However, until now no systematic review or meta-analysis have been conducted to estimate the true potentials of saliva for COVID-19 diagnosis. Our aim is to conduct a systematic review and meta-analysis on the available relevant literature in order to estimate the reliability and consistency of saliva specimens for COVID-19 diagnostics.

A new data mining approach as a short cut method is given for the determination of the copolymer composition from mass spectra. Our method simplifies the copolymer mass spectra by reduction of the number of mass peaks. The proposed procedure, namely the selection of the mass peaks, which is based on the most abundant peak of the mass spectrum, can be performed manually or more efficiently using our recently invented Mass-remainder analysis (MARA). The considerable reduction of the MS spectra also simplifies the calculation of the copolymer quantities for instance the number- and weight-average molecular weights (Mn and Mw, respectively), polydispersity index (Đ = Mw/Mn), average molar fraction (cA) and weight fraction (wA) of the comonomer A and so on. These copolymer properties are in line with those calculated by a reference method taking into account all the mass peaks of the copolymer distribution. We also suggest a highly efficient method and template for the determination of the composition drift by processing the reduced mass spectra.

Central nervous system (CNS) lymphoma is a rare and aggressive non-Hodgkin lymphoma that might arise in the CNS (primary CNS lymphoma) or disseminates from a systemic lymphoma to the CNS (secondary CNS lymphoma). Dysregulated expression of miRNAs is associated with various pathologic processes, and miRNA expression patterns may have diagnostic, prognostic, and therapeutic implications. However, miRNA expression is understudied in CNS lymphomas. We performed expression analysis of 798 miRNAs in 73 CNS lymphoma samples using the NanoString platform, followed by an analysis to identify potential diagnostic biomarkers characterizing subgroups and to examine differences based on their primary and secondary nature, molecular subtype, mutational patterns, and survival. Thirty-one differentially expressed miRNAs were identified between primary and secondary groups. In addition, 7 more miRNAs were identified associated with a molecular subtype and 25 associated with mutation status. Using unsupervised clustering methods, a small but distinct primary CNS lymphoma subgroup, with characteristically different expression patterns compared with the rest of the cases was defined. Finally, differentially regulated pathways were identified in the above comparisons and the utility of miRNA expression patterns in predicting survival was assessed. Our study identifies a novel CNS lymphoma subgroup defined by distinct miRNAs, proves the importance of specific miRNAs and pathways in the pathogenesis of CNS lymphomas, and provides the basis for future research in defining potential biomarkers.

A vevők megtartása az online kereskedők számára is lényeges feladattá vált, a lojalitás mérése mellett gyakran magában foglalja az újravásárlási és az ajánlási szándékot is. A vevőérték orientáció gondolata mentén kutatásunkban egy integrált modellt alkottunk, amely a vevők egyedi véleményformáló magatartási jellemzőivel kombinálja az adott vásárlás esetén felmerülő percepciókat (minőségészlelés, elégedettség, bizalom, hasznosság). A modell tesztelése PLS útelemzéssel történt egy 2000 fős online megkérdezés eredményei alapján. Eredményeink alapján a hat hatás bizonyult szignifikánsnak és erősnek (bizalom→eredményesség, minőség→bizalom, minőség→elégedettség, elégedettség→újravásárlási szándék, elégedettség→továbbajánlási szándék). Az offline véleményformáló magatartás hatása a továbbajánlási szándékra szignifikáns ugyan, de nagyon alacsony Cohen féle f2 értékkel rendelkezik. Hasonlóan, alacsony értéket (f2=0,07) kapunk az online véleményformáló magatartás esetében. Összességében azonban az integrált modell alapján felállítható összefüggések szerint, (az egyes exogén változókat mérve) előre jelezhetővé válik a vevők továbbajánlási és újravásárlási szándéka és ez egy nem monetáris ügyfélérték számítási alapját jelenti. --- Customer loyalty has become a key metric for e-tailers and it is frequently assessed “through the lenses” of repurchase and recommendation intention. Following the customer value oriented approach; we suggest an integrated SEM model, which combines important aspects of a webshop (quality perception, trust, usefulness, satisfaction) with the construct of online opinion leadership. Based on a large sample of online purchases we have tested the model with partial least square (PLS) path modelling. Results suggest that six of the suggested effects are strong (trust on usefulness, quality on trust and satisfaction, satisfaction on recommendation and repurchase intention). However, the offline opinion leadership construct has a low effect (Cohen's f2 value) on recommendation intention. The effect size of online opinion leadership on the same variable is higher, but still low (f2=0,07). The results are useful to predict online and offline word-of-mouth intentions and future purchases as well as to calculate a non-monetary customer value.