Pulmonary nocardiosis and scrub typhus in an immunocompromised host

Pulmonary infections are not uncommon in patients with an underlying immunocompromised condition. Unusual combination of microorganisms causing concomitant infections among these patients has also been reported. However, certain rare dual occurrences are usually unanticipated as in the case we present here. This case highlights the importance of being aware of the possible coexistence of infections in immunocompromised patients. To the best of our knowledge, this is the first report of coinfection with Nocardia otitidiscaviarum and Orientia tsutsugamushi in a critically ill immunocompromised patient from South India

Regional variations in antimicrobial susceptibility of community-acquired uropathogenic Escherichia coli in India: findings of a multicentric study highlighting the importance of local antibiograms

Background: Evidence-based prescribing is essential to optimise patient outcomes in cystitis. This requires knowledge of local antibiotic resistance rates. DASH to Protect Antibiotics (https://dashuti.com/) is a multicentric mentorship programme guiding centres in preparing, analysing and disseminating local antibiograms to promote antimicrobial stewardship in community UTI. Here we map the susceptibility profile of Escherichia coli from 22 Indian centres. Methods: These centres spanned 10 Indian States and three Union Territories. Antibiograms for urinary E. coli from the outpatient departments were collated. Standardisation was achieved by regional online training; anomalies were resolved via consultation with study experts. Data were collated and analysed. Findings: Nationally, fosfomycin, with 94% susceptibility (inter-centre range 83-97%), and nitrofurantoin with 85% susceptibility (61-97%) retained widest activity. Susceptibility rates were lower for co-trimoxazole (49%), fluoroquinolones (31%) and oral cephalosporins (26%). Rates for third- and fourth- generation cephalosporins were 46% and 52%, respectively, with 54% (33-58%) ESBL prevalence. Piperacillin-tazobactam (81%) amikacin (88%), meropenem (88%) retained better activity, but one centre in Delhi recorded only 42% meropenem susceptibility. Susceptibility rates were mostly higher in South, West and Northeast India; centres in the heavily-populated Gangetic plains, across North and Northwest India, had greater resistance. These findings highlight the importance of local antibiograms in guiding appropriate antimicrobial choices. Interpretation: Fosfomycin and nitrofurantoin are the preferred oral empirical choices for uncomplicated E. coli cystitis in India, though elevated resistance in some areas is concerning. Empiric use of fluoroquinolones and third generation cephalosporins is discouraged whereas piperacillin/tazobactam and aminoglycosides remain carbapenem-sparing parenteral agents

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.