Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Adopted Factorial and New In-Situ Micro-Designs for Stimulation of Matrix Acidizing of Carbonate Reservoir Rocks

Matrix acidizing has been developed in the petroleum industry for improving petroleum well productivity and minimizing near-wellbore damage. Mud acid (HF: HCl) has gained attractiveness in improving the porosity and permeability of reservoir formation. However, there are several challenges facing the use of mud acid, comprising its corrosive nature, high pH value, formation of precipitates, high reaction rate and quick consumption. Therefore, different acids have been developed to solve these problems, including organic-HF or HCl acids. Some of these acid combinations proved their effectiveness in being alternatives to mud acid in reservoir rock acidizing. The current research deals with a new acid combination based on Hydrochloric&ndash;Oxalic acids for acidizing carbonate core samples recovered from Qamchuqa Formation in Kirkuk oilfield, northern Iraq. A new in-situ micro-model adopted laboratory technique is utilized to study the microscale alteration and evolution of pore spaces, dissolved grains and identification of matrix acidizing characteristics. The in-situ micro-model is based on the injection of an identical dose of different concentrations of the new acid combination into thin section samples under an optical light microscope. The adopted procedure aims to provide unique and rapid information regarding the potential for texture and porosity modification that can be caused by the acidizing stimulation procedure. In connection, solubility tests for the untreated and treated reservoir core samples and the density of the combined acids after treatment are conducted based on designed experiments using response surface methodology (RSM). The effect of acid concentration [12% HCl: Oxalic acid (3.8&ndash;8.8%)] and acidizing temperature (from ambient to 78.8 &deg;C) on the solubility percentage of the samples and percentage increase in the combined acid density after acidizing were optimized and modeled. The obtained results confirm that the optimum dissolution of the core samples took place using 12% HCl:3.2% Oxalic acid with an optimum solubility (%) of the carbonate core rock of 53.78% at 21.7 &deg;C, while the optimum increase in density (%) of the combined acids was 1.54% at 78.3 &deg;C. The promising results could be employed for matrix acidizing of carbonate reservoir rocks for other oilfields

Adopted Factorial and New In-Situ Micro-Designs for Stimulation of Matrix Acidizing of Carbonate Reservoir Rocks

Matrix acidizing has been developed in the petroleum industry for improving petroleum well productivity and minimizing near-wellbore damage. Mud acid (HF: HCl) has gained attractiveness in improving the porosity and permeability of reservoir formation. However, there are several challenges facing the use of mud acid, comprising its corrosive nature, high pH value, formation of precipitates, high reaction rate and quick consumption. Therefore, different acids have been developed to solve these problems, including organic-HF or HCl acids. Some of these acid combinations proved their effectiveness in being alternatives to mud acid in reservoir rock acidizing. The current research deals with a new acid combination based on Hydrochloric–Oxalic acids for acidizing carbonate core samples recovered from Qamchuqa Formation in Kirkuk oilfield, northern Iraq. A new in-situ micro-model adopted laboratory technique is utilized to study the microscale alteration and evolution of pore spaces, dissolved grains and identification of matrix acidizing characteristics. The in-situ micro-model is based on the injection of an identical dose of different concentrations of the new acid combination into thin section samples under an optical light microscope. The adopted procedure aims to provide unique and rapid information regarding the potential for texture and porosity modification that can be caused by the acidizing stimulation procedure. In connection, solubility tests for the untreated and treated reservoir core samples and the density of the combined acids after treatment are conducted based on designed experiments using response surface methodology (RSM). The effect of acid concentration [12% HCl: Oxalic acid (3.8–8.8%)] and acidizing temperature (from ambient to 78.8 °C) on the solubility percentage of the samples and percentage increase in the combined acid density after acidizing were optimized and modeled. The obtained results confirm that the optimum dissolution of the core samples took place using 12% HCl:3.2% Oxalic acid with an optimum solubility (%) of the carbonate core rock of 53.78% at 21.7 °C, while the optimum increase in density (%) of the combined acids was 1.54% at 78.3 °C. The promising results could be employed for matrix acidizing of carbonate reservoir rocks for other oilfields

On the conversion of CO2 to value added products over composite PdZn and H-ZSM-5 catalysts: excess Zn over Pd, a compromise or a penalty?

A challenge in converting CO2 into hydrocarbons (HC) via methanol (MeOH) is the gap between the optimal temperature for each step (i.e. ≤250 °C for MeOH and ≥350 °C for HC). The focus of this study is to elucidate the cause of the limitations associated to oxygenate and hydrocarbon formation in combined PdZn and H-ZSM-5 catalysts at 250 to 350 °C. Starting with two different chemical states of Pd and Zn from two preparation approaches (physical mixture and surface organometallic chemistry grafting), operando X-ray absorption spectroscopy (XAS) and powder X-ray diffraction (PXRD) studies revealed similar PdZn alloy active phase formed during pretreatment in flowing H2/Inert at 400 °C. The physical mixture performed better than the grafted analogue, with up to 8.8% yield to oxygenates (MeOH and dimethyl ether (DME); MeOH+) at 300 °C, close to the estimated thermodynamic yield (9.0%). The space–time yield (STY) of oxygenates increased from 250 to 300 °C, reaching 8.5 mol(MeOH+) kg(PdZn)−1 h −1. The catalyst performance surpassed other reported yields in similar systems, which activity declined with temperature even below 300 °C. Operando XAS and PXRD experiments further showed that the PdZn phase active for MeOH formation was maintained during testing in the 250–350 °C range. InfraRed (FT-IR) and XAS experiments revealed the poisoning of Brønsted acid sites in H-ZSM-5 by Zn(II) exchange, thereby rendering it inactive for hydrocarbon formation. Overall, to avoid biasing the hybrid catalyst performance, a careful and balanced choice of the compositional characteristics will be crucial in designing an improved multi-functional catalytic system for CO2 valorisation

Contrast Induced Nephropathy:Efficacy of Matched Hydration and Forced Diuresis for prevention inpatients with impaired renal function undergoing coronary procedures - CINEMA Trial

BACKGROUND: Matched hydration and forced diuresis (MHFD) using the RenalGuard device has been shown to reduce contrast induced nephropathy (CIN) following coronary interventions. AIM: To evaluate the potential benefits of a non-automated MHFD protocol compared to current hydration protocol in prevention of CIN in patients with CKD. METHODS: A total of 1,205 patients were randomized to either non-automated MHFD group (n = 799) or intravenous hydration control group (n = 406). The MHFD group received 250 ml IV normal saline over 30 min before the coronary procedure followed by 0.5 mg/kg IV furosemide. Hydration infusion rate was manually adjusted to replace the patient's urine output. When urine output rate reached > 300 ml/h, patients underwent coronary procedure. Matched fluid replacement was maintained during the procedure and for 4-hour post-treatment. CIN was defined conventionally as ≥ 25% or ≥ 0.5 mg/dl rise in serum creatinine over baseline. RESULTS: CIN occurred in 121 of 1,205 (10.0%) patients in our study. With respect to the primary outcome, 64 (8.01%) of the MHFD patients developed CIN compared with 57 (14.04%) of the control group (p < 0.001). CONCLUSIONS: A non-automated MHFD protocol is an effective and safe method for the prevention of CIN in patients with CKD

Microwave-synthesized tin oxide nanocrystals for low-temperature solution-processed planar junction organo-halide perovskite solar cells

© 2017 The Royal Society of Chemistry. Tin oxide has been demonstrated to possess outstanding optoelectronic properties such as optical transparency and high electron mobility; therefore, it was successfully utilized as an electron transporting layer in various kinds of solar cells. In this study, for the first time, highly dispersible SnO 2 nanoparticles were synthesized by a microwave-assisted non-aqueous sol-gel route in an organic medium. Ethanol dispersion of the as-prepared nanoparticles was used to cast a uniform thin layer of SnO 2 without the aid of an aggregating agent and at low temperatures. Organohalide perovskite solar cells were fabricated using SnO 2 as the electron transporting layer. Morphological and spectroscopic investigations, in addition to the good photoconversion efficiency obtained, evidenced that the nanoparticles synthesized by this route have optimal properties such as small size and crystallinity to form a continuous film. Furthermore, this method allows high reproducibility and scalability of the film deposition process

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

Sentiment analysis is a technique for determining whether data is positive, negative, or neutral using Natural Language Processing (NLP). The particular challenge in classifying huge amounts of data is that it takes a long time and requires the employment of specialist human resources. Various deep learning techniques have been employed by different researchers to train and classify different datasets with varying outcomes. However, the results are not satisfactory. To address this challenge, this paper proposes a novel Sentiment Analysis approach based on Hybrid Neural Network Techniques. The preprocessing step is first applied to the Amazon Fine Food Reviews dataset in our architecture, which includes a number of data cleaning and text normalization techniques. The word embedding technique is then used to capture the semantics of the input by clustering semantically related inputs in the embedding space on the cleaned dataset. Finally, generated features were classified using three different deep learning techniques, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Hybrid CNN-RNN models, in two different ways, with each technique as follows: classification on the original feature set and classification on the reduced feature set based on Binary Coordinate Ascent (BCA) and Optimal Coordinate Ascent (OCA). The experimental results show that a hybrid CNN-RNN with the BCA and OCA algorithms outperforms state-of-the-art methods with 97.91% accuracy

Solution-processable MoO_x nanocrystals enable highly efficient reflective and semitransparent polymer solar cells

Solution-manufacturing of organic solar cells with best-in-class power conversion efficiency (PCE) will require all layers to be solution-coated without compromising solar cell performance. To date, the hole transporting layer (HTL) deposited on top of the organic bulk heterojunction layer in the inverted architecture is most commonly an ultrathin (&lt;10 nm) metal oxide layer prepared by vacuum-deposition. Here, we show that an alcohol-based nanocrystalline MoOx suspension with carefully controlled nanocrystal (NC) size can yield state of the art reflective and semitransparent solar cells. Using NCs smaller than the target HTL thickness (∼10 nm) can yield compact, pinhole-free films which result in highly efficient polymer:fullerene bulk heterojunction (BHJ) solar cells with PCE=9.5%. The solution processed HTL is shown to achieve performance parity with vacuum-evaporated HTLs for several polymer:fullerene combinations and is even shown to work as hole injection layer in polymer light emitting diodes (PLED). We also demonstrate that larger MoOx NCs (30–50 nm) successfully composite MoOx with Ag nanowires (NW) to form a highly conducting, transparent top anode with exceptional contact properties. This yields state-of-the-art semitransparent polymer: fullerene solar cells with PCE of 6.5% and overall transmission &gt;30%. The remarkable performance of reflective and semitransparent OPVs is due to the uncommonly high fill factors achieved using a carefully designed strategy for implementation of MoOx nanocrystals as HTL materials.</p

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

Sentiment analysis is a technique for determining whether data is positive, negative, or neutral using Natural Language Processing (NLP). The particular challenge in classifying huge amounts of data is that it takes a long time and requires the employment of specialist human resources. Various deep learning techniques have been employed by different researchers to train and classify different datasets with varying outcomes. However, the results are not satisfactory. To address this challenge, this paper proposes a novel Sentiment Analysis approach based on Hybrid Neural Network Techniques. The preprocessing step is first applied to the Amazon Fine Food Reviews dataset in our architecture, which includes a number of data cleaning and text normalization techniques. The word embedding technique is then used to capture the semantics of the input by clustering semantically related inputs in the embedding space on the cleaned dataset. Finally, generated features were classified using three different deep learning techniques, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Hybrid CNN-RNN models, in two different ways, with each technique as follows: classification on the original feature set and classification on the reduced feature set based on Binary Coordinate Ascent (BCA) and Optimal Coordinate Ascent (OCA). The experimental results show that a hybrid CNN-RNN with the BCA and OCA algorithms outperforms state-of-the-art methods with 97.91&#x0025; accuracy