The valve uptake index : improving assessment of prosthetic valve endocarditis and updating [ 18 F]FDG PET/CT(A) imaging criteria

Diagnosis of prosthetic valve endocarditis (PVE) by positron emission computed tomography angiography (PET/CTA) is based on visual and quantitative morpho-metabolic features. However, the fluorodeoxyglucose (FDG) uptake pattern can be sometimes visually unclear and susceptible to subjectivity. This study aimed to validate a new parameter, the valve uptake index [VUI, maximum standardized uptake value (SUVmax)−mean standardized uptake value (SUVmean)/SUVmax], designed to provide a more objective indication of the distribution of metabolic activity. Secondly, to re-evaluate the utility of traditionally used PVE imaging criteria and determine the potential value of adding the VUI in the diagnostic algorithm of PVE. Retrospective analysis of 122 patients (135 prosthetic valves) admitted for suspicion of endocarditis, with a conclusive diagnosis of definite (N = 57) or rejected (N = 65) PVE, and who had undergone a cardiac PET/CTA scan as part of the diagnostic evaluation. We measured the VUI and recorded the SUVmax, SUVratio, uptake pattern, and the presence of endocarditis-related anatomic lesions. The VUI, SUVmax, and SUVratio values were 0.54 ± 0.1 vs. 0.36 ± 0.08, 7.68 ± 3.07 vs. 3.72 ± 1.11, and 4.28 ± 1.93 vs. 2.16 ± 0.95 in the 'definite' PVE group vs. the 'rejected' group, respectively (mean ± SD; P 0.45 showed a sensitivity, specificity, and diagnostic accuracy for PVE of 85%, 88%, and 86.7% and increased diagnostic ability for confirming endocarditis when combined with the standard diagnostic criteria. The VUI demonstrated good diagnostic accuracy for PVE, even increasing the diagnostic power of the traditionally used morphometabolic parameters, which also confirmed their own diagnostic performance. More research is needed to assess whether the integration of the VUI into the PVE diagnostic algorithm may clarify doubtful cases and thus improve the diagnostic yield of PET/CTA

Diccionario de acontecimientos de derechas en el siglo XXI en América Latina

Este libro se origina fruto del diálogo y la construcción mancomunada del conocimiento en el Centro de Estudios Comparados de la Facultad de Ciencia Política y Relaciones Internacionales de la Universidad Nacional de Rosario y en el marco de la integración de preocupaciones e intereses de distintos proyectos de investigación radicados en esta universidad y en el CONICET. Esto explica la heterogeneidad que presentan los/as distintos autores/as de las entradas, desde académicos de trayectoria, investigadores en proceso de formación, hasta estudiantes avanzados. Diccionario de acontecimientos de derechas en el siglo XXI en América Latina es una obra de referencia para la reflexión profunda, articulada y sesuda de los itinerarios, derroteros y fisonomía que adquieren las derechas políticas en la región, en un siglo de vértigo, cambio y vorágine. Por ello, es una obra recomendada para quienes busquen auscultar la complejidad de la política regional en el siglo XXI; reconocer las facetas y lindes que adquieren las derechas en cada uno de los países cuando se tornan visibles en el espacio público político; y, finalmente, advertir las continuidades y rupturas de la historia política contemporánea de América Latina a la luz de sus pliegues y acontecimientos más destacados.Fil: Iglesias, Esteban. Universidad Nacional de Rosario. Facultad de Ciencia Política y Relaciones Internacionales; Argentina

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.