Meat species authentication in food is most commonly based on the detection of genetic variations. Official food control laboratories frequently apply single and multiplex real-time polymerase chain reaction (PCR) assays and/or DNA arrays. However, in the near future, DNA metabarcoding, the generation of PCR products for DNA barcodes, followed by massively parallel sequencing by next generation sequencing (NGS) technologies, could be an attractive alternative. DNA metabarcoding is superior to well-established methodologies since it allows simultaneous identification of a wide variety of species not only in individual foodstuffs but even in complex mixtures. We have recently published a DNA metabarcoding assay for the identification and differentiation of 15 mammalian species and six poultry species. With the aim to harmonize analytical methods for food authentication across EU Member States, the DNA metabarcoding assay has been tested in an interlaboratory ring trial including 15 laboratories. Each laboratory analyzed 16 anonymously labelled samples (eight samples, two subsamples each), comprising six DNA extract mixtures, one DNA extract from a model sausage, and one DNA extract from maize (negative control). Evaluation of data on repeatability, reproducibility, robustness, and measurement uncertainty indicated that the DNA metabarcoding method is applicable for meat species authentication in routine analysis.

As the number of people affected by COVID-19 disease caused by the novel coronavirus SARS-CoV-2 ebbs and flows in different national and sub-national regions across the world, it is evident that our lifestyle and socio-economic trajectories will have to be adapted and adjusted to the changing scenarios. Novel forecasting tools and frameworks provide an arguable advantage to facilitate this adapting and adjusting process, by promoting efficient resource management at individual and institutional levels. Based on deterministic compartment models we propose an empirical top-down modeling approach to provide epidemic forecasts and risk calculations for (local) outbreaks. We use neural networks to develop leading indicators based on available data for different regions. These indicators are not only used to assess the risk of a (new) outbreak or to determine the effectiveness of a measure at an early stage, but also in parametric models to determine an effective forecast, along with the associated uncertainty. Based on initial results, we show the performance of such an approach and its robustness against inherent disturbances in epidemiological surveillance data. We foresee such a statistical framework to drive web-based automatic platforms to democratize the dissemination of prognosis results.

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

The post-harvest processing of coffee beans leads to a wide range of reactions involving proteins. The formation of crosslinks between proteins and phenolic compounds present in high concentrations of coffee beans represents one of the most challenging and still not fully characterized reactions. The aim of this work was to assess the presence of products from such reactions in coffee samples, focusing on the adducts between cysteine and chlorogenic acids (CQAs). For this purpose, 19 green and 15 roasted coffee samples of the Coffea arabica, Coffea canephora, and Coffea liberica varieties were selected for this study and basically characterized. Then, targeted liquid chromatography mass spectrometry (LC-MS/MS) methods were developed to assess the formation of adducts between CQA and cysteine, glutathione, and N-acetylcysteine as the amino acid and peptide models, and quantified such adducts in coffee samples. The results of the characterization showed a heterogeneous distribution of the protein content (8.7–14.6%), caffeine (0.57–2.62 g/100 g), and antioxidant capacity (2–4.5 g ascorbic acid/100 g) in Arabica, Canephora, and Liberica samples. Glutamic acid, arginine, and proline were found to be the major amino acids, while 5-CQA (38–76%), 3-CQA (4–13%), and 4-CQA (4–13%) were the most abundant CQA derivatives of all coffee varieties. The model experiments for adduct formation demonstrated that cysteine binds to CQA via thiol groups and 5-CQA initially isomerizes to 3- and 4-CQA, depending on the conditions, allowing cysteine to bind to two different sites on 3-, 4- or 5-CQA molecules, thus, forming six different Cys-CQA adducts with m/z 476. The reaction was more favored at pH 9, and the adducts proved to be stable up to 90 °C for 10 min and up to 28 days at room temperature. The relative quantification of adducts showed peak area values ranging from 1100 to 3000 in green coffee bean samples, while no adducts were detected in roasted coffee beans. Overall, this work was the first attempt to demonstrate the presence of Cys-CQA adducts in coffee beans and paves the way for further investigations of such adduct formation at the protein level.

Adverse effects of drug combinations and their underlying mechanisms are highly relevant for safety evaluation, but often not fully studied. Hydroxychloroquine (HCQ) and azithromycin (AZM) were used as a combination therapy in the treatment of COVID-19 patients at the beginning of the pandemic, leading to higher complication rates in comparison to respective monotherapies. Here, we used human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to systematically investigate the effects of HCQ, AZM, and their combination on the structure and functionality of cardiomyocytes, and to better understand the underlying mechanisms. Our results demonstrate synergistic adverse effects of AZM and HCQ on electrophysiological and contractile function of iPSC-CMs. HCQ-induced prolongation of field potential duration (FPDc) was gradually increased during 7-day treatment period and was strongly enhanced by combination with AZM, although AZM alone slightly shortened FPDc in iPSC-CMs. Combined treatment with AZM and HCQ leads to higher cardiotoxicity, more severe structural disarrangement, more pronounced contractile dysfunctions, and more elevated conduction velocity, compared to respective monotreatments. Mechanistic insights underlying the synergistic effects of AZM and HCQ on iPSC-CM functionality are provided based on increased cellular accumulation of HCQ and AZM as well as increased Cx43- and Nav1.5-protein levels.

The growing number of wheat-related allergies worldwide has resulted in a new trend towards gluten-free alternatives. In this context, alternative cereals such as sorghum and oats are attracting new interest. Given the limited data available, the question of whether these cereals are completely safe and gluten-free for allergy sufferers remains open. One of the key steps in protein research is their efficient extraction. In this work, the Osborne sequential extraction method was developed and optimized using the response surface methodology in order to fractionate oat proteins. An optimized desirability of 0.986 was achieved with an extraction time of 4.7 min, a speed of 6, and a sample/solvent ratio of 5. The corresponding optimized responses were 8.7, 4.0, and 5.1% for the extraction yields of the avenin, avenalin, and albumin/globulin fractions, respectively. Further characterization of the extracts was carried out on 24 homogeneous and commercial oat samples via LC-MS/MS, targeting six potentially allergenic proteins. The avenin-E protein featured prominently, with relative contents of 60.7, 32.2, 58.0, and 59.8% in the total extract, avenin, avenalin, and albumin/globulin fractions, respectively, while the Avenin-3, ATI-2, avenin, SSG2, and SSG1 proteins in the total extract showed levels of 16.4, 9.3, 6.6, 4.8, and 2.2%, respectively. The preliminary results of an ELISA performed on the different fractions revealed low levels of gluten (from 1.24 ± 0.14 to 3.61 ± 0.16 mg/kg), which were well below the threshold limit of 20 mg/kg. These results support the hypothesis that oats can be a safe food for people suffering from cereal-related allergies. These results open the door to further studies into the comprehensive characterization of oat proteins.