Antibiotic Resistance Trends of Gram-negative Bacteria Most Frequently Isolated from Inpatients in a Tertiary Care Hospital in Sana'a, Yemen

Objective: To determine the trends of antibiotic resistance of Gram-negative bacteria, most frequently isolated from inpatients at the University of Science and Technology Hospital (USTH) in Sana'a, Yemen. Methods: A retrospective, cross-sectional study on the antibiotic resistance of Gram-negative bacteria most frequently isolated from respiratory tract, pus, urine, blood and other types of specimens from inpatients admitted to the USTH. Data were retrieved from the hospital records of culture-positive inpatients in the period from January 2006 to December 2013, and annual trends of resistance were compared using chi-square test for trends at P values < 0.05. Results: Of 2005 Gram-negative bacterial isolates in the period from 2006 to 2013, the most frequently isolated species were Escherichia coli (41.6%), Acinetobacter species (26.7%), Klebsiella species (21.0%) and Pseudomonas aeruginosa (10.6%). Amikacin and carbapenems were the most active drugs against E. coli, with a decrease in the susceptibility of this species to the third- and fourth-generation cephalosporins and a variable resistance rate to quinolones that significantly increased in 2013. Acinetobacter species susceptibility to most antibiotics decreased significantly over the years of the study, where polymyxin B was the only one found to be effective against this species. On the other hand, the trend of Klebsiella species resistance to imipenem, piperacillin-tazobactam, cefepime, ceftazidime increased over the years of the study. Susceptibility of Klebsiella species to ciprofloxacin, levofloxacin and moxifloxacin showed fluctuations, while the susceptibility of aminoglycosides (amikacin and gentamicin) and ampicillin-sulbactam showed no difference. The resistance of P. aeruginosa to the majority of antibiotics was not dramatically changed over the years of the study period, but gentamicin resistance rate was considerably dropped from 77.8% in 2008 to 25.9% in 2013. Conclusions: Of the most frequently isolated Gram-negative bacteria in Sana'a, Acinetobacter species has the highest resistance rate to the most commonly used antibiotics, where only polymyxin B is effective against this species. P. aeruginosa shows an unchanging rate of resistance to antibiotics in the USTH despite being quite resistant to antibiotics on a global scale, which could be attributed to the smaller number of P. aeruginosa isolates tested over the study period. Further large-scale studies on the trends of antibiotic resistance rates in hospital-based settings and the best ways to counteract such resistance in Yemen are recommended. &nbsp

Requirements analysis document

This document details the purpose, features, and expected interfaces for the complete EXCELERATE WP9 (Use Case D: ELIXIR framework for secure archiving, dissemination and analysis of human access-controlled data). It outlines the tasks the system will perform, the constraints under which it operates, and how it reacts in certain circumstances. This document is intended for stakeholders, designers and developers as well as users of the system, and derives from a joint analysis carried out with these groups

5-Isopropyl­imidazolidine-2,4-dione monohydrate

In the title compound, C6H10N2O2·H2O, the imidazole ring is essentially planar, with a maximum deviation of 0.012 (2) Å. In the crystal, mol­ecules are connected via N—H⋯O and O—H⋯O hydrogen bonds, forming a supra­molecular tape along the a axis

GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome

Background: Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation. Findings: We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered. Conclusions: Through a combination of streamlined data acquisition, interoperable representation of dataset collections, and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no.This work was supported by the Research Council of Norway (under grant agreements 221580, 218241, and 231217/F20), by the Norwegian Cancer Society (under grant agreements 71220’PR-2006-0433 and 3485238-2013), and by the South-Eastern Norway Regional Health Authority (under grant agreement 2014041).Peer Reviewe

The global burden of adolescent and young adult cancer in 2019 : a systematic analysis for the Global Burden of Disease Study 2019

Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A Fuzzy-Logic Based Coordinated Scheduling Technique for Inter-grid Architectures

International audienceInter-grid is a composition of small interconnected grid domains; each has its own local broker. The main challenge is to devise appropriate job scheduling policies that can satisfy goals such as global load balancing together with maintaining the local policies of the different domains. Existing inter-grid methodologies are based on either centralised meta-scheduling or decentralised scheduling which carried is out by local brokers, but without proper coordination. Both are suitable interconnecting grid domains, but breaks down when the number of domains become large. Earlier we proposed Slick, a scalable resource discovery and job scheduling technique for broker based interconnected grid domains, where inter-grid scheduling decisions are handled by gateway schedulers installed on the local brokers. This paper presents a decentralised scheduling technique for the Slick architecture, where cross-grid scheduling decisions are made using a fuzzy-logic based algorithm. The proposed technique is tested through simulating its implementation on 512 interconnected Condor pools. Compared to existing techniques, our results show that the proposed technique is better at maintaining the overall throughput and load balancing with increasing number of interconnected grids

A DATA-DRIVEN SEMI-GLOBAL ALIGNMENT TECHNIQUE FOR MASQUERADE DETECTION IN STAND-ALONE AND CLOUD COMPUTING SYSTEMS Inventors: Hesham Abdelazim Ismail Mohamed KHOLIDY Fabrizio Baiardi

A masquerade attacker impersonates a legal user to utilize the user services and privileges. The semi-global alignment algorithm (SGA) is one of the most effective and efficient techniques to detect these attacks but it has not reached yet the accuracy and performance required by large scale, multiuser systems. To improve both the effectiveness and the performances of this algorithm, we propose the Data-Driven Semi-Global Alignment, DDSGA approach. From the security effectiveness view point, DDSGA improves the scoring systems by adopting distinct alignment parameters for each user. Furthermore, it tolerates small mutations in user command sequences by allowing small changes in the low-level representation of the commands functionality. It also adapts to changes in the user behaviour by updating the signature of a user according to its current behaviour. To optimize the runtime overhead, DDSGA minimizes the alignment overhead and parallelizes the detection and the update. After describing the DDSGA phases, we present the experimental results that show that DDSGA achieves a high hit ratio of 88.4 percent with a low false positive rate of 1.7 percent. It improves the hit ratio of the enhanced SGA by about 21.9 percent and reduces Maxion-Townsend cost by 22.5 percent. Hence, DDSGA results in improving both the hit ratio and false positive rates with an acceptable computational overhead

A finite state hidden markov model for predicting multistage attacks in cloud systems

Cloud computing significantly increased the security threats because intruders can exploit the large amount of cloud resources for their attacks. However, most of the current security technologies do not provide early warnings about such attacks. This paper presents a Finite State Hidden Markov prediction model that uses an adaptive risk approach to predict multi-staged cloud attacks. The risk model measures the potential impact of a threat on assets given its occurrence probability. The attacks prediction model was integrated with our autonomous cloud intrusion detection framework (ACIDF) to raise early warnings about attacks to the controller so it can take proactive corrective actions before the attacks pose a serious security risk to the system. According to our experiments on DARPA 2000 dataset, the proposed prediction model has successfully fired the early warning alerts 39.6 minutes before the launching of the LLDDoS1.0 attack. This gives the auto response controller ample time to take preventive measures.NPRP grant # 09-778-2-299 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu