Genetic clustering and parentage analysis of Western Balkan grapevines (Vitis vinifera L.)

A total of 90 grapevine samples collected in five countries of the Western Balkan region were evaluated for trueness-to-type and kinship relations based on comparative analysis with 1,130 grapevine genotypes held at the INRA "Domaine de Vassal" French Grape Germplasm Repository, using 14 microsatellite markers. In the context of the comparative analysis, twenty-four synonyms/counterparts and the putative parents for twelve Balkan accessions were identified. We discovered five pairs of homonyms, subsequently confirming the identity or parentage of three of them. Some of the examined accessions were identified either on the basis of the genotypes found in the literature, or through parentage relationships revealed in this study. For the remaining fifty accessions we were unable to establish either their pedigree or to identify them on the basis of SSR profiles available elsewhere. Finally, the Balkan genotypes that were not well classified by synonymy or parentage analysis were further studied with a Principal Coordinate Analysis to reveal genetic clustering within larger datasets of genotypes. The graphical display of the individual and group distances showed that about forty accessions (85 %) are structured within a group of Balkan and Eastern Europe genotypes and only a minor proportion resulted in admixed population assignment

Burden of varicella in Central and Eastern Europe : findings from a systematic literature review

Funding Information: The authors take full responsibility for the scope, direction, and content of the manuscript, and have approved the submitted manuscript. Medical writing assistance was provided by Eleanor Finn of PAREXEL International and was funded by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. The authors wish to thank the following for contributions in development of the manuscript: Barbara J. Kuter, PhD, MPH, Global Vaccines Medical Affairs, and Tracey J. Weiss, Center for Observational and Real-World Evidence (CORE), Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. Funding Information: The study was funded by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. Funding Information: J. Wysocki received travel grants to attend international scientific conferences and fees for lectures from Pfizer and payment from a grant sponsored by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. I. Ivaskeviciene has received a USA travel grant to attend international scientific meeting, from Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth. M. Pokorn has received a research grant from Pfizer and payment for lectures from Pfizer, Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA and GSK. L. Jancoriene has received travel grants to attend international scientific conferences and fees for lectures from Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, AbbVie and Pfizer and payment for a clinical study sponsored by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. J. Pluta and L.J. Wolfson are employees of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, and stockholders of Merck & Co., Inc., Kenilworth, NJ, USA. Publisher Copyright: © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Introduction: Vaccination against varicella rapidly reduces disease incidence, resulting in reductions in both individual burden and societal costs. Despite these benefits, there is no standardization of varicella immunization policies in Europe, including countries in Central and Eastern Europe (CEE). Areas covered: This systematic literature review identified publications on the epidemiology of varicella, its associated health and economic burden, and vaccination strategies within the CEE region, defined as Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Serbia, Slovakia, and Slovenia. Twenty-six studies were identified from a search of PubMed, Embase®, and MEDLINE® biomedical literature databases, supplemented by gray literature and country-specific/global websites. Expert commentary: Limited information exists in published studies on the burden of varicella in CEE. The wide variability in incidence rates between countries is likely explained by a lack of consistency in reporting systems. Funded universal varicella vaccination (UVV) in CEE is currently available only in Latvia as a one-dose schedule, but Hungary together with Latvia are introducing a two-dose strategy in 2019. For countries that do not provide UVV, introduction of vaccination is predicted to provide substantial reductions in cases and rates of associated complications, with important economic benefits.publishersversionPeer reviewe

Raising AWaRe-ness of Antimicrobial Stewardship Challenges in Pediatric Emergency Care: Results from the PERFORM Study Assessing Consistency and Appropriateness of Antibiotic Prescribing Across Europe

Background Optimization of antimicrobial stewardship is key to tackling antimicrobial resistance, which is exacerbated by overprescription of antibiotics in pediatric emergency departments (EDs). We described patterns of empiric antibiotic use in European EDs and characterized appropriateness and consistency of prescribing. Methods Between August 2016 and December 2019, febrile children attending EDs in 9 European countries with suspected infection were recruited into the PERFORM (Personalised Risk Assessment in Febrile Illness to Optimise Real-Life Management) study. Empiric systemic antibiotic use was determined in view of assigned final “bacterial” or “viral” phenotype. Antibiotics were classified according to the World Health Organization (WHO) AWaRe classification. Results Of 2130 febrile episodes (excluding children with nonbacterial/nonviral phenotypes), 1549 (72.7%) were assigned a bacterial and 581 (27.3%) a viral phenotype. A total of 1318 of 1549 episodes (85.1%) with a bacterial and 269 of 581 (46.3%) with a viral phenotype received empiric systemic antibiotics (in the first 2 days of admission). Of those, the majority (87.8% in the bacterial and 87.0% in the viral group) received parenteral antibiotics. The top 3 antibiotics prescribed were third-generation cephalosporins, penicillins, and penicillin/β-lactamase inhibitor combinations. Of those treated with empiric systemic antibiotics in the viral group, 216 of 269 (80.3%) received ≥1 antibiotic in the “Watch” category. Conclusions Differentiating bacterial from viral etiology in febrile illness on initial ED presentation remains challenging, resulting in a substantial overprescription of antibiotics. A significant proportion of patients with a viral phenotype received systemic antibiotics, predominantly classified as WHO Watch. Rapid and accurate point-of-care tests in the ED differentiating between bacterial and viral etiology could significantly improve antimicrobial stewardship

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

Background: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. Methods: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. Findings: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. Interpretation: This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics

A Novel Framework for Phenotyping Children With Suspected or Confirmed Infection for Future Biomarker Studies

Copyright © 2021 Nijman, Oostenbrink, Moll, Casals-Pascual, von Both, Cunnington, De, Eleftheriou, Emonts, Fink, van der Flier, de Groot, Kaforou, Kohlmaier, Kuijpers, Lim, Maconochie, Paulus, Martinon-Torres, Pokorn, Romaine, Calle, Schlapbach, Smit, Tsolia, Usuf, Wright, Yeung, Zavadska, Zenz, Levin, Herberg, Carrol and the PERFORM consortium (Personalized Risk assessment in febrile children to optimize Real-life Management across the European Union).Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI. Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0– <16 years: the Alder Hey emergency department (n = 1,120), Alder Hey pediatric intensive care unit (n = 355), Erasmus emergency department (n = 1,993), Maasstad emergency department (n = 714) and St. Mary's hospital (n = 200) cohorts. Biomarkers including procalcitonin (PCT) (4 cohorts), neutrophil gelatinase-associated lipocalin-2 (NGAL) (3 cohorts) and resistin (2 cohorts) were compared for their ability to classify patients according to current standards (dichotomous classification of SBI vs. non-SBI), vs. a proposed PERFORM classification algorithm that assign patients to one of eleven categories. These categories were based on clinical phenotype, test outcomes and C-reactive protein level and accounted for the uncertainty of final diagnosis in many febrile children. The success of the biomarkers was measured by the Area under the receiver operating Curves (AUCs) when they were used individually or in combination. Results: Using the new PERFORM classification system, patients with clinically confident bacterial diagnosis (“definite bacterial” category) had significantly higher levels of PCT, NGAL and resistin compared with those with a clinically confident viral diagnosis (“definite viral” category). Patients with diagnostic uncertainty had biomarker concentrations that varied across the spectrum. AUCs were higher for classification of “definite bacterial” vs. “definite viral” following the PERFORM algorithm than using the “SBI” vs. “non-SBI” classification; summary AUC for PCT was 0.77 (95% CI 0.72–0.82) vs. 0.70 (95% CI 0.65–0.75); for NGAL this was 0.80 (95% CI 0.69–0.91) vs. 0.70 (95% CI 0.58–0.81); for resistin this was 0.68 (95% CI 0.61–0.75) vs. 0.64 (0.58–0.69) The three biomarkers combined had summary AUC of 0.83 (0.77–0.89) for “definite bacterial” vs. “definite viral” infections and 0.71 (0.67–0.74) for “SBI” vs. “non-SBI.” Conclusion: Biomarkers of bacterial infection were strongly associated with the diagnostic categories using the PERFORM classification system in five independent cohorts. Our proposed algorithm provides a novel framework for phenotyping children with suspected or confirmed infection for future biomarker studies.publishersversionPeer reviewe

Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

Background: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. Methods: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). Results: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%–98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%–97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. Conclusions: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study.

BACKGROUND Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. INTERPRETATION This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics. FUNDING European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation