The informal social support for autonomy and dependence in pain inventory Spanish version

Social support plays a crucial role in the quality of life of people with chronic pain. The Informal Social Support for Autonomy and Dependence in Pain Inventory assesses two functions of received social support: the promotion of autonomy and the promotion of dependence. The aim of this cross-sectional study was to adapt this instrument for its use in the Spanish population. The sample comprised 256 individuals with chronic pain. Participants were recruited through two local associations of people with fibromyalgia, a physiotherapy unit and a hospital pain unit. The data were collected in Spain between October 2018 and January 2020. The structure of the questionnaire was analysed using confirmatory factor analysis, average variance extracted, composite reliability and internal consistency indexes, and inter-correlations between the scales. The criterion-related validity of the instrument was analysed by investigating its relationship with pain intensity, positive and negative affect, daily functioning, activity impairment, wellbeing and satisfaction with life. The structure with the best fit had four related factors: emotional social support for the promotion of autonomy; instrumental social support for the promotion of autonomy; emotional social support for the promotion of dependence and instrumental social support for the promotion of dependence. The scales showed adequate internal consistency. An association was found between higher levels of instrumental social support for the promotion of dependence and higher levels of pain-related disability and decreased daily functioning. An association was also found between the promotion of autonomy and increased satisfaction with life. The Spanish version of the inventory shows appropriate psychometric properties. In the setting of disability prevention, this instrument is useful in assessing the support relationships between people with chronic pain and their relatives.This study was supported by grants from the Spanish Ministry of Science and Innovation (PID2019-106086RB-I00) and the Regional Government of Andalusia (HUM-566). Funding for open access charge: Universidad de Málaga / CBUA

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.