Modelando SN 2016gkg, la supernova argentina

Fil: Orellana, Mariana D. Universidad Nacional de Río Negro; ArgentinaFil: Orellana, Mariana D. CONICET; ArgentinaFil: Bersten, Melina C. Universidad Nacional de La Plata. Instituto de Astrofísica de La Plata (IALP), CCT-CONICET; ArgentinaFil: Folatelli, Gastón. Universidad Nacional de La Plata. Instituto de Astrofísica de La Plata (IALP), CCT-CONICET; ArgentinaFil: Benvenuto, Omar. Universidad Nacional de La Plata. Instituto de Astrofísica de La Plata (IALP), CCT-CONICET; ArgentinaFil: García, Federico. Instituto Argentino de Radioastronomía, CONICET-CIC; ArgentinaFil: Buso, Victor. Observatorio Astronómico Busoniano, Rosario; ArgentinaFil: Sanchez, José L. Observatorio Astronómico Geminis Austral, Rosario; ArgentinaVíctor Buso, an amateur astronomer from Rosario, discovered what would be called SN 2016gkg in galaxy NGC 613, recording in unique form photometric data of the arrival of the shockwave to the surface of a star when it was exploding as a supernova (SN). After the discovery, intensive campaigns were carried out to monitor it, which allowed classify it as Type IIb. In our case, we have photometric and spectroscopic data that can be interpreted differently than the one proposed by other authors, and are consistent with the pre-explosion images of the Hubble space telescope. We establish comparisons with SN 2011dh, a similar SN that has been well studied in the context of those originated in interacting binary systems, and present an hydrodynamic model that can account for the unique change of brightness of SN 2016gkg in different phases.Víctor Buso, un astrónomo aficionado de Rosario, descubrió lo que se llamaría SN 2016gkg en la galaxia NGC 613, registrando en forma única datos fotométricos de la llegada de la onda de choque a la superficie de una estrella cuando explotaba como una supernova (SN). Después del descubrimiento, se llevaron a cabo campañas intensivas para monitorearlo, lo que permitió clasificarlo como Tipo IIb. En nuestro caso, tenemos datos fotométricos y espectroscópicos que pueden interpretarse de manera diferente a la propuesta por otros autores, y son consistentes con las imágenes previas a la explosión del telescopio espacial Hubble. Establecemos comparaciones con SN 2011dh, un SN similar que ha sido bien estudiado en el contexto de aquellos originados en sistemas binarios interactivos, y presentamos un modelo hidrodinámico que puede explicar el cambio único de brillo de SN 2016gkg en diferentes fases

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic

Using genomics to understand antimicrobial resistance and transmission in Neisseria gonorrhoeae

Gonorrhoea infections are on the increase and strains that are resistant to all antimicrobials used to treat the disease have been found worldwide. These observations encouraged the World Health Organization to include Neisseria gonorrhoeae on their list of high-priority organisms in need of new treatments. Fortunately, concurrent resistance to both antimicrobials used in dual therapy is still rare. The fight against antimicrobial resistance (AMR) must begin from an understanding of how it evolves and spreads in sexual networks. Genome-based analyses have allowed the study of the gonococcal population dynamics and transmission, giving a novel perspective on AMR gonorrhoea. Here, we will review past, present and future treatment options for gonorrhoea and explain how genomics is helping to increase our understanding of the changing AMR and transmission landscape. This article contains data hosted by Microreact. </p

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics

Genomic determinants of speciation and spread of the Mycobacterium tuberculosis complex

Models on how bacterial lineages differentiate increase our understanding of early bacterial speciation events and the genetic loci involved. Here, we analyze the population genomics events leading to the emergence of the tuberculosis pathogen. The emergence is characterized by a combination of recombination events involving core pathogenesis functions and purifying selection on early diverging loci. We identify the phoR gene, the sensor kinase of a two-component system involved in virulence, as a key functional player subject to pervasive positive selection after the divergence of the Mycobacterium tuberculosis complex from its ancestor. Previous evidence showed that phoR mutations played a central role in the adaptation of the pathogen to different host species. Now, we show that phoR mutations have been under selection during the early spread of human tuberculosis, during later expansions, and in ongoing transmission events. Our results show that linking pathogen evolution across evolutionary and epidemiological time scales points to past and present virulence determinants

Adaptation to the cervical environment is associated with increased antibiotic susceptibility in Neisseria gonorrhoeae

Neisseria gonorrhoeae is an urgent public health threat due to rapidly increasing incidence and antibiotic resistance. In contrast with the trend of increasing resistance, clinical isolates that have reverted to susceptibility regularly appear, prompting questions about which pressures compete with antibiotics to shape gonococcal evolution. Here, we used genome-wide association to identify loss-of-function (LOF) mutations in the efflux pump mtrCDE operon as a mechanism of increased antibiotic susceptibility and demonstrate that these mutations are overrepresented in cervical relative to urethral isolates. This enrichment holds true for LOF mutations in another efflux pump, farAB, and in urogenitally-adapted versus typical N. meningitidis, providing evidence for a model in which expression of these pumps in the female urogenital tract incurs a fitness cost for pathogenic Neisseria. Overall, our findings highlight the impact of integrating microbial population genomics with host metadata and demonstrate how host environmental pressures can lead to increased antibiotic susceptibility