Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis

Background: Infections due to antibiotic-resistant bacteria are threatening modern health care. However, estimating their incidence, complications, and attributable mortality is challenging. We aimed to estimate the burden of infections caused by antibiotic-resistant bacteria of public health concern in countries of the EU and European Economic Area (EEA) in 2015, measured in number of cases, attributable deaths, and disability-adjusted life-years (DALYs). Methods: We estimated the incidence of infections with 16 antibiotic resistance–bacterium combinations from European Antimicrobial Resistance Surveillance Network (EARS-Net) 2015 data that was country-corrected for population coverage. We multiplied the number of bloodstream infections (BSIs) by a conversion factor derived from the European Centre for Disease Prevention and Control point prevalence survey of health-care-associated infections in European acute care hospitals in 2011–12 to estimate the number of non-BSIs. We developed disease outcome models for five types of infection on the basis of systematic reviews of the literature. Findings: From EARS-Net data collected between Jan 1, 2015, and Dec 31, 2015, we estimated 671 689 (95% uncertainty interval [UI] 583 148–763 966) infections with antibiotic-resistant bacteria, of which 63·5% (426 277 of 671 689) were associated with health care. These infections accounted for an estimated 33 110 (28 480–38 430) attributable deaths and 874 541 (768 837–989 068) DALYs. The burden for the EU and EEA was highest in infants (aged <1 year) and people aged 65 years or older, had increased since 2007, and was highest in Italy and Greece. Interpretation: Our results present the health burden of five types of infection with antibiotic-resistant bacteria expressed, for the first time, in DALYs. The estimated burden of infections with antibiotic-resistant bacteria in the EU and EEA is substantial compared with that of other infectious diseases, and has increased since 2007. Our burden estimates provide useful information for public health decision-makers prioritising interventions for infectious diseases

Improving the Clinical Diagnosis of Influenza—a Comparative Analysis of New Influenza A (H1N1) Cases

BACKGROUND: The presentation of new influenza A(H1N1) is broad and evolving as it continues to affect different geographic locations and populations. To improve the accuracy of predicting influenza infection in an outpatient setting, we undertook a comparative analysis of H1N1(2009), seasonal influenza, and persons with acute respiratory illness (ARI) in an outpatient setting. METHODOLOGY/PRINCIPAL FINDINGS: Comparative analyses of one hundred non-matched cases each of PCR confirmed H1N1(2009), seasonal influenza, and ARI cases. Multivariate analysis was performed to look for predictors of influenza infection. Receiver operating characteristic curves were constructed for various combinations of clinical and laboratory case definitions. The initial clinical and laboratory features of H1N1(2009) and seasonal influenza were similar. Among ARI cases, fever, cough, headache, rhinorrhea, the absence of leukocytosis, and a normal chest radiograph positively predict for both PCR-confirmed H1N1-2009 and seasonal influenza infection. The sensitivity and specificity of current WHO and CDC influenza-like illness (ILI) criteria were modest in predicting influenza infection. However, the combination of WHO ILI criteria with the absence of leukocytosis greatly improved the accuracy of diagnosing H1N1(2009) and seasonal influenza (positive LR of 7.8 (95%CI 3.5-17.5) and 9.2 (95%CI 4.1-20.3) respectively). CONCLUSIONS/SIGNIFICANCE: The clinical presentation of H1N1(2009) infection is largely indistinguishable from that of seasonal influenza. Among patients with acute respiratory illness, features such as a temperature greater than 38 degrees C, rhinorrhea, a normal chest radiograph, and the absence of leukocytosis or significant gastrointestinal symptoms were all positively associated with H1N1(2009) and seasonal influenza infection. An enhanced ILI criteria that combines both a symptom complex with the absence of leukocytosis on testing can improve the accuracy of predicting both seasonal and H1N1-2009 influenza infection

Elucidation of single atom catalysts for energy and sustainable chemical production: Synthesis, characterization and frontier science

The emergence of single atom sites as a frontier research area in catalysis has sparked extensive academic and industrial interest, especially for energy, environmental and chemicals production processes. Single atom catalysts (SACs) have shown remarkable performance in a variety of catalytic reactions, demonstrating high selectivity to the products of interest, long lifespan, high stability and more importantly high atomic metal utilization efficiency. In this review, we unveil in depth insights on development and achievements of SACs, including (a) Chronological progress on SACs development, (b) Recent advances in SACs synthesis, (c) Spatial and temporal SACs characterization techniques, (d) Application of SACs in different energy and chemical production, (e) Environmental and economic aspects of SACs, and (f) Current challenges, promising ideas and future prospects for SACs. On a whole, this review serves to enlighten scientists and engineers in developing fundamental catalytic understanding that can be applied into the future, both for academia or valorizing chemical processes

Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review

The material characteristics and properties of transition metal dichalcogenide (TMDCs) have gained research interest in various fields, such as electronics, catalytic, and energy storage. In particular, many researchers have been focusing on the applications of TMDCs in dealing with environmental pollution. TMDCs provide a unique opportunity to develop higher-value applications related to environmental matters. This work highlights the applications of TMDCs contributing to pollution reduction in (i) gas sensing technology, (ii) gas adsorption and removal, (iii) wastewater treatment, (iv) fuel cleaning, and (v) carbon dioxide valorization and conversion. Overall, the applications of TMDCs have successfully demonstrated the advantages of contributing to environmental conversation due to their special properties. The challenges and bottlenecks of implementing TMDCs in the actual industry are also highlighted. More efforts need to be devoted to overcoming the hurdles to maximize the potential of TMDCs implementation in the industry

Co-pyrolysis of Chlorella vulgaris with plastic wastes: Thermal degradation, kinetics and Progressive Depth Swarm-Evolution (PDSE) neuro network-based optimization

The search of sustainable route for biofuel production from renewable biomass have garnered wide interest to seek for various routes without compromising the environment. Co-pyrolysis emerges as a promising thermochemical route that can improve the pyrolysis output from simultaneously processing more than two feedstocks in an inert atmosphere. This paper focuses on the kinetic modeling and neuro-evolution optimization in the application of catalytic co-pyrolysis of microalgae and plastic waste using HZSM-5 supported on limestone (HZSM-5/LS), in which co-pyrolysis of binary mixture of microalgae and plastic wastes (i.e. High-Density Polyethylene and Low-Density Polyethylene) was investigated over different heating rates. The results have shown a positive synergistic effect between the microalgae and polyethylene in which the apparent activation energies values have reduced significantly ( 20 kJ/mol) compared to that obtained by pyrolysis of individual microalgae component. The kinetic models reflect that the mixture of microalgae and Low-Density Polyethylene for use as co-pyrolysis feedstock requires activation energy that is 23% and 13% lower compared to that required by pure microalgae and the mixture of microalgae and High-Density Polyethylene, respectively. The Progressive Depth Swarm-Evolution (PDSE) was used for neural architecture search, which subsequently provided optimal reaction condition at 873 K can achieve 99.6 % of degradation rate using a tri-combination of LDPE (0.13 %) + HDPE (0.77 %) + MA (0.11 %) in the presence of HZSM-5/LS catalyst

Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization

The aim of this study is to identify the optimum thermal conversion of Chlorella vulgaris with neuro-evolutionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of catalytic thermal degradation of Chlorella vulgaris. Results showed that the proposed method can generate predictions which are more accurate compared to other conventional approaches (>90% lower in Root Mean Square Error (RMSE) and Mean Bias Error (MBE)). In addition, Simulated Annealing is proposed to determine the optimal operating conditions for microalgae conversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of 900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion

Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization

The aim of this study is to identify the optimum thermal conversion of Chlorella vulgaris with neuro-evolutionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of catalytic thermal degradation of Chlorella vulgaris. Results showed that the proposed method can generate predictions which are more accurate compared to other conventional approaches (>90% lower in Root Mean Square Error (RMSE) and Mean Bias Error (MBE)). In addition, Simulated Annealing is proposed to determine the optimal operating conditions for microalgae conversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of 900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts. Fundamentally, PCA is used to prioritise statistically significant process variables based on their respective contribution scores. The variables with high contribution score are then optimised by the experiment-based optimisation methodology. By doing so, the number of experiments run for process optimisation and process changes can be reduced by efficient prioritisation. Process cycle assessment ensures that no negative environmental impact is caused by the optimisation result. As a proof of concept, this framework is implemented in a real oil re-refining plant. The overall product yield was improved by 55.25% while overall product quality improved by 20.6%. Global Warming Potential (GWP) and Acidification Potential (AP) improved by 90.89% and 3.42% respectively

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts. Fundamentally, PCA is used to prioritise statistically significant process variables based on their respective contribution scores. The variables with high contribution score are then optimised by the experiment-based optimisation methodology. By doing so, the number of experiments run for process optimisation and process changes can be reduced by efficient prioritisation. Process cycle assessment ensures that no negative environmental impact is caused by the optimisation result. As a proof of concept, this framework is implemented in a real oil re-refining plant. The overall product yield was improved by 55.25% while overall product quality improved by 20.6%. Global Warming Potential (GWP) and Acidification Potential (AP) improved by 90.89% and 3.42% respectively

The Early Clinical Features of Dengue in Adults: Challenges for Early Clinical Diagnosis

Dengue infection in adults has become increasingly common throughout the world. As most of the clinical features of dengue have been described in children, we undertook a prospective study to determine the early symptoms and signs of dengue in adults. We show here that, overall, dengue cases presented with high rates of symptoms listed in the WHO 1997 or 2009 classification schemes for probable dengue fever thus resulting in high sensitivities of these schemes when applied for early diagnosis. However, symptoms such as myalgia, arthralgia, retro-orbital pain and mucosal bleeding were less frequently reported in older adults. This trend resulted in reduced sensitivity of the WHO classification schemes in older adults even though they showed increased risks of hospitalization and severe dengue. Instead, we suggest that older adults who present with fever and leukopenia should be tested for dengue, even in the absence of other symptoms. This could be useful for early clinical diagnosis in older adults so that they can be monitored and treated for severe dengue, which is especially important when an antiviral drug becomes available