Pedersentopp3291

BACKGROUND OXA-244, a single amino acid variant of OXA-48, demonstrates weaker hydrolytic activity towards carbapenems and temocillin compared with OXA-48. Of note, these antimicrobials are present in high concentrations in several carbapenemase-producing Enterobacterales (CPE) screening media. As a result, some screening media fail to grow OXA-244-producing isolates, while the prevalence of OXA-244 producers is constantly increasing in France. METHODS Here, we evaluate the performance of three commercially available CPE screening media [ChromID® CARBA SMART (bioMérieux), Brilliance™ CRE (Thermo Fisher) and mSuperCARBA™ (MAST Diagnostic)] for their ability to detect OXA-244 producers (n = 101). As OXA-244 producers may also express an ESBL, two additional ESBL screening media were tested (Brilliance™ ESBL and ChromID® BLSE). MICs of temocillin and imipenem were determined by broth microdilution. The clonality of OXA-244-producing Escherichia coli isolates (n = 97) was assessed by MLST. RESULTS Overall, the sensitivity of the ChromID® CARBA SMART, Brilliance™ CRE and mSuperCARBA™ media were 14% (95% CI = 8.1%-22.5%), 54% (95% CI = 43.3%-63.4%) and 99% (95% CI = 93.8%-100%), respectively, for the detection of OXA-244 producers. Among the 101 OXA-244-producing isolates, 96% were E. coli and 77%-78% grew on ESBL screening media. MLST analysis identified five main STs among OXA-244-producing E. coli isolates ST38 (n = 37), ST361 (n = 17), ST69 (n = 12), ST167 (n = 11) and ST10 (n = 8). CONCLUSIONS Our results demonstrated that the mSuperCARBA™ medium is very efficient in the detection of OXA-244 producers, unlike the ChromID® CARBA SMART medium. The high prevalence of ESBLs among OXA-244 producers allowed detection of 77%-78% of them using ESBL-specific screening media. © The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please email journals.permissions@oup.com.OBJECTIVES To characterize amikacin population pharmacokinetics in patients with hypoalbuminaemia and to develop a model-based interactive application for amikacin initial dosage. METHODS A population pharmacokinetic model was developed using a non-linear mixed-effects modelling approach (NONMEM) with amikacin concentration-time data collected from clinical practice (75% hypoalbuminaemic patients). Goodness-of-fit plots, minimum objective function value, prediction-corrected visual predictive check, bootstrapping, precision and bias of parameter estimates were used for model evaluation. An interactive model-based simulation tool was developed in R (Shiny and R Markdown). Cmax/MIC ratio, time above MIC and AUC/MIC were used for optimizing amikacin initial dose recommendation. Probabilities of reaching targets were calculated for the dosage proposed. RESULTS A one-compartment model with first-order linear elimination best described the 873 amikacin plasma concentrations available from 294 subjects (model develoed by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please email journals.permissions@oup.com.A female advantage in social cognition (SoC) might contribute to women's underrepresentation in autism (ASD). The latter could be underpinned by sex differences in social brain structure. This study investigated the relationship between structural social brain networks and social cognition in females and males in relation to ASD and autistic traits in twins. We used a co-twin design, in 77 twin pairs (39 female) aged 12.5 to 31.0 years. Twin pairs were discordant or concordant for ASD or autistic traits, discordant or concordant for other neurodevelopmental disorders, or concordant for neurotypical development. They underwent structural magnetic resonance imaging and were assessed for SoC using the naturalistic Movie for the Assessment of Social Cognition (MASC). Autistic traits predicted reduced SoC capacities predominantly in male twins, despite a comparable extent of autistic traits in each sex, although the association between SoC and autistic traits did not differ significantly between the sexes. Consistently, within-pair associations between SoC and social brain structure revealed that lower SoC ability was associated with increased cortical thickness of several brain regions, particularly in males. Our findings confirm the notion that sex differences in social cognition in association with ASD are underpinned by sex differences in brain structure. © The Author(s) 2020. Published by Oxford University Press.PURPOSE To determine the influence of optimal collimation during lumbar spine radiography on radiation dose and image quality. PR-957 MATERIAL AND METHODS 110 lumbar spine patients were split into two groups-the first imaged with standard collimation and the second with optimal collimation. Body mass index, image field size, exposure conditions and dose area product were measured. Effective and absorbed organ doses were calculated. Image quality was assessed. RESULTS Optimal collimation reduced the primary field by up to 40%. The effective dose was reduced by 48% for the AP projection, while no differences were found for the LAT projection due to incorrect positioning of the central beam with standard collimation. The absorbed dose to selected radiosensitive organs decreased by 41 and 10% in the AP and LAT projections, respectively. Image quality for the LAT projection improved by 24% and maintained for the AP projection. CONCLUSION Optimal collimation in lumbar spine imaging significantly influences patient exposure to radiation. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.N-glycosylation is one of the most important posttranslational modifications of proteins. It plays important roles in the biogenesis and functions of proteins by influencing their folding, intracellular localization, stability and solubility. N-glycans are synthesized by glycosyltransferases, a complex group of ubiquitous enzymes that occur in most kingdoms of life. A growing body of evidence shows that N-glycans may influence processing and functions of glycosyltransferases, including their secretion, stability and substrate/acceptor affinity. Changes in these properties may have a profound impact on glycosyltransferase activity. Indeed, some glycosyltransferases have to be glycosylated themselves for full activity. N-glycans and glycosyltransferases play roles in the pathogenesis of many diseases (including cancers), so studies on glycosyltransferases may contribute to the development of new therapy methods and novel glycoengineered enzymes with improved properties. In this review we focus on the role of N-glycosylation in the activity of glycosyltransferases and attempt to summarize all available data about this phenomenon. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological systems, the discovery of complex diseases and the search for therapeutic drugs. However, the increase in data also increases the difficulty of biological networks analysis. Therefore, algorithms that can handle large, heterogeneous and complex data are needed to better analyze the data of these network structures and mine their useful information. Deep learning is a branch of machine learning that extracts more abstract features from a larger set of training data. Through the establishment of an artificial neural network with a network hierarchy structure, deep learning can extract and screen the input information layer by layer and has representation learning ability. The improved deep learning algorithm can be used to process complex and heterogeneous graph data structures and is increasingly being applied to the mining of network data information. In this paper, we first introduce the used network data deep learning models. After words, we summarize the application of deep learning on biological networks. Finally, we discuss the future development prospects of this field. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Four in ten young rural Chinese children are 'left behind' by parents migrating for economic opportunities. Left-behind children do as well academically and imagine as many possible futures for themselves as their peers, implying that they must compensate in some ways for loss of everyday contact with their parents. Three studies test and find support for the prediction that compensation entails self-expansion to include a caregiving grandmother rather than one's mother in self-concept, as is typical in Chinese culture. We measured self-expansion with feeling, function, and neurophysiological variables. Twelve-year-old middle school left-behind children (Study 1, N = 66) and 20-year-old formerly left-behind children (now in college, Studies 2, 3, N = 162) felt closer to their grandmothers and not as close to their mothers as their peers. Self-expansion had functional consequence (spontaneous depth-of-processing) and a left a neurophysiological trace (event-related potential, Study 3). Left-behind participants had enhanced recall for information incidentally connected to grandmothers (Studies 1 and 3, not Study 2). Our results provide important insights into how left-behind children cope with the loss of parental presence they include their grandmother in their sense of self. Future studies are needed to test downstream consequences for emotional and motivational resilience. © The Author(s) 2020. Published by Oxford University Press.Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing 'Black-box' approaches that are unable to reveal causal relationships from large amounts of initially encoded features. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.