Editorial: Surveying Antimicrobial Resistance: The New Complexity of the Problem

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A novel gluten knowledge base of potential biomedical and health-related interactions extracted from the literature: using machine learning and graph analysis methodologies to reconstruct the bibliome

Background In return for their nutritional properties and broad availability, cereal crops have been associated with different alimentary disorders and symptoms, with the majority of the responsibility being attributed to gluten. Therefore, the research of gluten-related literature data continues to be produced at ever-growing rates, driven in part by the recent exploratory studies that link gluten to non-traditional diseases and the popularity of gluten-free diets, making it increasingly difficult to access and analyse practical and structured information. In this sense, the accelerated discovery of novel advances in diagnosis and treatment, as well as exploratory studies, produce a favourable scenario for disinformation and misinformation. Objectives Aligned with, the European Union strategy “Delivering on EU Food Safety and Nutrition in 2050″ which emphasizes the inextricable links between imbalanced diets, the increased exposure to unreliable sources of information and misleading information, and the increased dependency on reliable sources of information; this paper presents GlutKNOIS, a public and interactive literature-based database that reconstructs and represents the experimental biomedical knowledge extracted from the gluten-related literature. The developed platform includes different external database knowledge, bibliometrics statistics and social media discussion to propose a novel and enhanced way to search, visualise and analyse potential biomedical and health-related interactions in relation to the gluten domain. Methods For this purpose, the presented study applies a semi-supervised curation workflow that combines natural language processing techniques, machine learning algorithms, ontology-based normalization and integration approaches, named entity recognition methods, and graph knowledge reconstruction methodologies to process, classify, represent and analyse the experimental findings contained in the literature, which is also complemented by data from the social discussion. Results and conclusions In this sense, 5814 documents were manually annotated and 7424 were fully automatically processed to reconstruct the first online gluten-related knowledge database of evidenced health-related interactions that produce health or metabolic changes based on the literature. In addition, the automatic processing of the literature combined with the knowledge representation methodologies proposed has the potential to assist in the revision and analysis of years of gluten research. The reconstructed knowledge base is public and accessible at https://sing-group.org/glutknois/Fundação para a Ciência e a Tecnologia | Ref. UIDB/50006/2020Xunta de Galicia | Ref. ED481B-2019-032Xunta de Galicia | Ref. ED431G2019/06Xunta de Galicia | Ref. ED431C 2022/03Universidade de Vigo/CISU

Effect of phenolic compounds extracted from chestnut (Castanea sativa Mill) industry by-products on antibiotic resistant bacteria

In the last years, antibiotic resistance has become a selious threat tohuman and animal health since a large number of microorganisms have developed resistance to the drugs commonly used.Several natural compounds have been gcttingincreased attention as alternatives to many drugs. Phenolic compounds are secondary metabolites which exhibit several properties, such as, antimicrobial. antioxidant, anti-inflammatory, antimutagentic and cardioprotective. Thus, this study aims to investigate the antibacterial properties of the iJhenolic compounds extracted from the chestnut (Castaneasativa Mill.) industry by-products !gains! antibiotic resistant bacteria.info:eu-repo/semantics/publishedVersio

A framework to extract biomedical knowledge from gluten-related tweets: the case of dietary concerns in digital era

Journal pre proofBig data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from more than 400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry-LAQV, which is financed by national funds from and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020] units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the European Regional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte, the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of [ED431C2018/55GRC] Competitive Reference Group. The authors also acknowledge the post-doctoral fellowship [ED481B2019032] of Martin PerezPerez, funded by the Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUGinfo:eu-repo/semantics/publishedVersio

A Decade-Long Commitment to Antimicrobial Resistance Surveillance in Portugal

Antimicrobial resistance (AMR) is a worldwide problem with serious health and economic repercussions. Since the 1940s, underuse, overuse, and misuse of antibiotics have had a significant environmental downside. Large amounts of antibiotics not fully metabolized after use in human and veterinary medicine, and other applications, are annually released into the environment. The result has been the development and dissemination of antibiotic-resistant bacteria due to many years of selective pressure. Surveillance of AMR provides important information that helps in monitoring and understanding how resistance mechanisms develop and disseminate within different environments. Surveillance data is needed to inform clinical therapy decisions, to guide policy proposals, and to assess the impact of action plans to fight AMR. The Functional Genomics and Proteomics Unit, based at the University of Trás-os-Montes and Alto Douro (UTAD) in Vila Real, Portugal, has recently completed 10 years of research surveying AMR in bacteria, mainly commensal indicator bacteria such as enterococci and Escherichia coli from the microbiota of different animals. Samples from more than 75 different sources have been accessed, from humans to food-producing animals, pets, and wild animals. The typical microbiological workflow involved phenotypic studies followed by molecular approaches. Throughout the decade, 4,017 samples were collected and over 5,000 bacterial isolates obtained. High levels of AMR to several antimicrobial classes have been reported, including to β-lactams, glycopeptides, tetracyclines, aminoglycosides, sulphonamides and quinolones. Multi-resistant strains, some relevant to human and veterinary medicine like extended-spectrum β-lactamase-producing E. coli and vancomycin-resistant enterococci, have been repeatedly isolated even in non-synanthropic animal species. Of particular relevance are reports of AMR bacteria in wildlife from natural reserves and endangered species. Future work awaits as this threatening yet unsolved problem persists

Review of structural features and binding capacity of polyphenols to gluten proteins and peptides in vitro: Relevance to celiac disease

UIDB 50006/2020 UID/AGR/04033/2019 project-POCI-01-0145-FEDER-029068Polyphenols have been extensively studied due to their beneficial effects on human health, particularly for the prevention and treatment of diseases related to oxidative stress. Nevertheless, they are also known to have an anti-nutritional effect in relation to protein metabolism. This effect is a consequence of its binding to digestive enzymes and/or protein substrates. Dietary gluten is the main trigger of celiac disease, a common immune-based disease of the small intestine and for which the only treatment available is the adherence to a gluten-free diet. Recent studies have addressed the use of dietary polyphenols to interact with gluten proteins and avoid its downstream deleterious effects, taking the advantage of the anti-nutritive nature of polyphenols by protein sequestering. Flavonoids, coumarins and tannins have shown the ability to form insoluble complexes with gluten proteins. One of the most promising molecules has been epigallocatechin-3-gallate, which through its binding to gliadins, was able to reduce gliadins digestibility and its ability to stimulate monolayer permeability and transepithelial transport of immunodominant peptides in cell models. This review focuses on the structural features and binding capacity of polyphenols to gluten proteins and peptides, and the prospects of developing an adjuvant therapy in celiac disease.publishersversionpublishe

Use social media knowledge for exploring the portuguese wine industry: following talks and perceptions?

This work presents an exploratory study that retrieves, processes, and analyses Twitter data to gain insights about the relevance and perceptions of the wine industry in the Douro Portuguese region (including Porto and Douro wines), as well as other regions in the country. The main techniques and algorithms used in our work belong to the families of natural language processing and machine learning, and the practical relevance of the proposed methodology has been proven in the analysis of 1.2 million unique messages from more than 764,000 distinct users retrieved from the Twitter platform. Derived results from this study are valuable to provide insights that can be further used in the context of Business Informatics to promote better and more efficient marketing campaigns, for example, centering the topic on the most interested people or communicating with the most appropriate words.,is work was supported by the Associate Laboratory for Green Chemistry—LAQV, financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020; the Portuguese Foundation for Sci ence and Technology (FCT/MCTES) under the scope of the strategic funding of UIDB/04469/2020 unit and Bio TecNorte operation funded by the European Regional De velopment Fund (ERDF) under the scope of Norte2020—Programa Operacional Regional do Norte. Ref. NORTE-01-0145-FEDER-000004; the Conseller´ıa de Edu cacion, Universidades e Formaci ´ on Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigacion de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06; and Portuguese Foundation for Science and Technology for a PhD Grant (SFRH/BD/145497/2019).info:eu-repo/semantics/publishedVersio

Multilocus Sequence Typing of Vancomycin-Resistant Enterococcus faecium isolated from pigs in Portugal

Vancomycin-resistant enterococci (VRE) first appeared in the late 1980s in a few European countries. In the last two decades, however, vancomycin-resistant Enterococcus faecium (VREfm) as became an emergent and challenging nosocomial problem. Specific clonal groups of E. faecium show an enhanced capacity to disseminate in the nosocomial setting. These strains can be assigned to distinct clonal groups or complexes based on DNA sequence-based typing (multi-locus sequence typing - MLST). In this context, we used the MLST technic to study the clonal relatedness of 18 VREfm strains previously isolated from pigs at slaughter level, in Portugal. These strains have been phenotypic and genotypic characterized in a previous study (1). For this purpose, internal 400–600-bp fragments of housekeeping genes were amplified and sequenced: adk, atpA, ddl, gdh, gyd, purK and pst (2). The sequences obtained were analysed and compared against the http://mlst.ucc.ie/ database. The combination of the seven obtained alleles, for each isolate, allows us to determine the corresponding sequence type (ST) and clonal complex (CC). MLST analyses revealed sequence type 5 (ST5) (n =5) and ST139 (n=12). These E. faecium sequence types belong to clonal complex 5 (CC5). Although ST139 is farthest from ST5, from which differs in three alleles, it also belongs to CC5. Strains belonging to CC5 are recognized to be circulating among European pigs. Although E. faecium CC5 are commonly found among animals they have also been isolated from humans. Furthermore, four of the isolates assigned to ST5 showed high-level resistance (HLR) to kanamycin and streptomycin, what can be of concern. In case of severe enterococcal infections the synergistic and bactericidal therapy can be reliably achieved with the addition of an aminoglycoside to β-lactamic antibiotics (or other cell wall agent such as vancomycin), as long as the organism does not exhibit HLR to the aminoglycoside. The recovery of E. faecium CC5 clone from slaughtered animals is of concern, since these strains may have the ability to either colonize humans or cause human infections

Multilocus Sequence Typing for characterization of potential risk ESBLs-producing Escherichia coli isolated from pigs, including strains of new singletons ST2528, ST2524 and ST2525

Infections caused by Escherichia coli harboring extended-spectrum beta-lactamases (ESBL) have a tremendous impact on public health, because of treatment complications. ESBL-producing E. coli are increasingly reported in healthy food-producing animals that can spread to humans either by direct contact or, more importantly, through the food chain. Here we describe a molecular survey aimed at determining the population structure and dynamics of ESBL-producing E. coli strains recovered from healthy pigs slaughtered for human consumption in Portugal. For this purpose, a total of 71 faecal samples from pigs were collected (2008 to 2009) in different geographical regions of Portugal. Susceptibility to 16 antibiotics was tested by disk-diffusion method in all recovered isolates and ESBL detection was carried out by double-disk test. PCR and sequencing methods characterized blaESBL genes responsible for the ESBL-phenotype. In addition, we used multilocus sequence typing (MLST) to identify the genetic lineages of all ESBL-producing E. coli strains, which were characterized by sequencing the internal fragments of 7 housekeeping genes (adk, fumC, gyrB, icd, mdh, purA, recA); the MLST database was used to determine allelic profiles and for sequence type (ST) and clonal complex (CC) assignment. Among the 35 ESBL-producing strains, MLST analysis revealed 9 different STs under 6 CCs and 9 singletons STs. The CC10 and CC155 were the most common CCs, with 4 and 11 isolates, respectively. Two other isolates were assigned to the CC101. Moreover, 5 strains were included in 3 new STs; 3 of them were identified in a new allele for the fumC gene that originated the new ST2528; in addition, 2 isolates were registered as ST2524 and ST2525 through new combination of alleles. Through the MLST database we found that ST656 (CC10) and ST8 (CC165) have a higher homology to ST2524 and ST2525, respectively. However, by the definition of CCs, ST2524 and ST2525 most likely belong to CC10 and CC165, respectively. Our data shows the presence of ESBL producing E. coli isolates in pigs slaughtered for human consumption and raises important questions in the potential risk factors to public health due to the transmission of bacteria carrying resistance through the food chain, and spreading resistance to other bacteria of human clinical significance. A great heterogeneity of MLST types was observed, among which CC10, CC155 and CC101 have already been associated with human clinical isolates