Fitting the integrated Spectral Energy Distributions of Galaxies

Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time, attempting to keep up with the vastly increased volume and quality of available data. We review here the field of SED fitting, describing the modelling of ultraviolet to infrared galaxy SEDs, the creation of multiwavelength data sets, and the methods used to fit model SEDs to observed galaxy data sets. We touch upon the achievements and challenges in the major ingredients of SED fitting, with a special emphasis on describing the interplay between the quality of the available data, the quality of the available models, and the best fitting technique to use in order to obtain a realistic measurement as well as realistic uncertainties. We conclude that SED fitting can be used effectively to derive a range of physical properties of galaxies, such as redshift, stellar masses, star formation rates, dust masses, and metallicities, with care taken not to over-interpret the available data. Yet there still exist many issues such as estimating the age of the oldest stars in a galaxy, finer details ofdust properties and dust-star geometry, and the influences of poorly understood, luminous stellar types and phases. The challenge for the coming years will be to improve both the models and the observational data sets to resolve these uncertainties. The present review will be made available on an interactive, moderated web page (sedfitting.org), where the community can access and change the text. The intention is to expand the text and keep it up to date over the coming years.Comment: 54 pages, 26 figures, Accepted for publication in Astrophysics & Space Scienc

ARGONAUT II study of the in vitro activity of plazomicin against carbapenemase-producing klebsiella pneumoniae

Plazomicin was tested against 697 recently acquired carbapenemresistant Klebsiella pneumoniae isolates from the Great Lakes region of the United States. Plazomicin MIC50 and MIC90 values were 0.25 and 1 mg/liter, respectively; 680 isolates (97.6%) were susceptible (MICs of ≤2 mg/liter), 9 (1.3%) intermediate (MICs of 4 mg/liter), and 8 (1.1%) resistant (MICs of>32 mg/liter). Resistance was associated with rmtF-, rmtB-, or armA-encoded 16S rRNA methyltransferases in all except 1 isolate

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Plazomicin activity against polymyxin-resistant Enterobacteriaceae, including MCR-1-producing isolates.

Plazomicin, a novel aminoglycoside with in vitro activity against MDR Gram-negative organisms, is under development to treat patients with serious enterobacterial infections. We evaluated the activity of plazomicin and comparators against colistin-resistant enterobacterial isolates. Susceptibility to plazomicin and comparators was tested by broth microdilution for a collection of 95 colistin-resistant enterobacterial isolates collected from 29 hospitals in eight countries. Forty-two isolates (Klebsiella pneumoniae and Klebsiella oxytoca) possessed chromosomally encoded resistance mechanisms to colistin, 21 isolates (Escherichia coli and Salmonella enterica) expressed the mcr-1 gene, 8 isolates (Serratia, Proteus, Morganella and Hafnia) were intrinsically resistant to colistin and 24 isolates (K. pneumoniae, E. coli and Enterobacter spp.) had undefined, non-mcr-1 mechanisms. Susceptibility profiles were defined according to CLSI for aminoglycosides and to EUCAST for colistin and tigecycline. Plazomicin inhibited 89.5% and 93.7% of the colistin-resistant enterobacterial isolates at ≤ 2 and ≤4 mg/L, respectively. MICs of plazomicin were ≤2 mg/L for all of the mcr-1 positive isolates and ≤4 mg/L for all the intrinsic colistin-resistant Enterobacteriaceae. Non-susceptibility to currently marketed aminoglycosides was common: amikacin, 16.8%; gentamicin, 47.4%; and tobramycin, 63.2%. Plazomicin was the most potent aminoglycoside tested with an MIC90 of 4 mg/L, compared with 32, &gt;64 and 64 mg/L for amikacin, gentamicin and tobramycin, respectively. Plazomicin displayed potent activity against colistin-resistant clinical enterobacterial isolates, including those expressing the mcr-1 gene. Plazomicin was more active than other aminoglycosides against this collection of isolates. The further development of plazomicin for the treatment of infections due to MDR Enterobacteriaceae is warranted