Accuracy of LightCycler SeptiFast for the detection and identification of pathogens in the blood of patients with suspected sepsis: a systematic review and meta-analysis

Purpose: There is an urgent need to develop diagnostic tests to improve the detection of pathogens causing life-threatening infection (sepsis). SeptiFast is a CE-marked multi-pathogen real-time PCR system capable of detecting DNA sequences of bacteria and fungi present in blood samples within a few hours. We report here a systematic review and meta-analysis of diagnostic accuracy studies of SeptiFast in the setting of suspected sepsis.

Methods: A comprehensive search strategy was developed to identify studies that compared SeptiFast with blood culture in suspected sepsis. Methodological quality was assessed using QUADAS. Heterogeneity of studies was investigated using a coupled forest plot of sensitivity and specificity and a scatter plot in receiver operator characteristic space. Bivariate model method was used to estimate summary sensitivity and specificity.

Results: From 41 phase III diagnostic accuracy studies, summary sensitivity and specificity for SeptiFast compared with blood culture were 0.68 (95 % CI 0.63–0.73) and 0.86 (95 % CI 0.84–0.89) respectively. Study quality was judged to be variable with important deficiencies overall in design and reporting that could impact on derived diagnostic accuracy metrics.

Conclusions: SeptiFast appears to have higher specificity than sensitivity, but deficiencies in study quality are likely to render this body of work unreliable. Based on the evidence presented here, it remains difficult to make firm recommendations about the likely clinical utility of SeptiFast in the setting of suspected sepsis.

Using SILAC proteomics to investigate the effect of the mycotoxin, alternariol, in the human H295R steroidogenesis model

The mycotoxin alternariol (AOH) is an important contaminant of fruits and cereal products. The current study sought to address the effect of a non-toxic AOH concentration on the proteome of the steroidogenic H295R cell model. Quantitative proteomics based on stable isotope labeling by amino acids in cell culture (SILAC) coupled to 1D-SDS-PAGE-LC-MS/MS was applied to subcellular-enriched protein samples. Gene ontology (GO) and ingenuity pathway analysis (IPA) were further carried out for functional annotation and identification of protein interaction networks. Furthermore, the effect of AOH on apoptosis and cell cycle distribution was also determined by the use of flow cytometry analysis. This work identified 22 proteins that were regulated significantly. The regulated proteins are those involved in early stages of steroid biosynthesis (SOAT1, NPC1, and ACBD5) and C21-steroid hormone metabolism (CYP21A2 and HSD3B1). In addition, several proteins known to play a role in cellular assembly, organization, protein synthesis, and cell cycle were regulated. These findings provide a new framework for studying the mechanisms by which AOH modulates steroidogenesis in H295R cell model. 

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.

Experimental validation of a drive-by stiffness identification method for bridge monitoring

An experimental investigation is carried out to verify the feasibility of using an instrumented vehicle to detect and monitor bridge dynamic parameters. The low-cost method consists of the use of a moving vehicle fitted with accelerometers on its axles. In the laboratory experiment, the vehicle–bridge interaction model consists of a scaled two-axle vehicle model crossing a simply supported steel beam. The bridge model also includes a scaled road surface profile. The effects of varying the vehicle model configuration and speed are investigated. A finite element beam model is calibrated using the experimental results, and a novel algorithm for the identification of global bridge stiffness is validated. Using measured vehicle accelerations as input to the algorithm, the beam stiffness is identified with a reasonable degree of accuracy.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.

Development of numerical methodologies for parameter identification and shape optimization in metal forming simulations

Por parte da indústria de estampagem tem-se verificado um interesse crescente em simulações numéricas de processos de conformação de chapa, incluindo também métodos de engenharia inversa. Este facto ocorre principalmente porque as técnicas de tentativa-erro, muito usadas no passado, não são mais competitivas a nível económico. O uso de códigos de simulação é, atualmente, uma prática corrente em ambiente industrial, pois os resultados tipicamente obtidos através de códigos com base no Método dos Elementos Finitos (MEF) são bem aceites pelas comunidades industriais e científicas Na tentativa de obter campos de tensão e de deformação precisos, uma análise eficiente com o MEF necessita de dados de entrada corretos, como geometrias, malhas, leis de comportamento não-lineares, carregamentos, leis de atrito, etc.. Com o objetivo de ultrapassar estas dificuldades podem ser considerados os problemas inversos. No trabalho apresentado, os seguintes problemas inversos, em Mecânica computacional, são apresentados e analisados: (i) problemas de identificação de parâmetros, que se referem à determinação de parâmetros de entrada que serão posteriormente usados em modelos constitutivos nas simulações numéricas e (ii) problemas de definição geométrica inicial de chapas e ferramentas, nos quais o objetivo é determinar a forma inicial de uma chapa ou de uma ferramenta tendo em vista a obtenção de uma determinada geometria após um processo de conformação. São introduzidas e implementadas novas estratégias de otimização, as quais conduzem a parâmetros de modelos constitutivos mais precisos. O objetivo destas estratégias é tirar vantagem das potencialidades de cada algoritmo e melhorar a eficiência geral dos métodos clássicos de otimização, os quais são baseados em processos de apenas um estágio. Algoritmos determinísticos, algoritmos inspirados em processos evolucionários ou mesmo a combinação destes dois são usados nas estratégias propostas. Estratégias de cascata, paralelas e híbridas são apresentadas em detalhe, sendo que as estratégias híbridas consistem na combinação de estratégias em cascata e paralelas. São apresentados e analisados dois métodos distintos para a avaliação da função objetivo em processos de identificação de parâmetros. Os métodos considerados são uma análise com um ponto único ou uma análise com elementos finitos. A avaliação com base num único ponto caracteriza uma quantidade infinitesimal de material sujeito a uma determinada história de deformação. Por outro lado, na análise através de elementos finitos, o modelo constitutivo é implementado e considerado para cada ponto de integração. Problemas inversos são apresentados e descritos, como por exemplo, a definição geométrica de chapas e ferramentas. Considerando o caso da otimização da forma inicial de uma chapa metálica a definição da forma inicial de uma chapa para a conformação de um elemento de cárter é considerado como problema em estudo. Ainda neste âmbito, um estudo sobre a influência da definição geométrica inicial da chapa no processo de otimização é efetuado. Este estudo é realizado considerando a formulação de NURBS na definição da face superior da chapa metálica, face cuja geometria será alterada durante o processo de conformação plástica. No caso dos processos de otimização de ferramentas, um processo de forjamento a dois estágios é apresentado. Com o objetivo de obter um cilindro perfeito após o forjamento, dois métodos distintos são considerados. No primeiro, a forma inicial do cilindro é otimizada e no outro a forma da ferramenta do primeiro estágio de conformação é otimizada. Para parametrizar a superfície livre do cilindro são utilizados diferentes métodos. Para a definição da ferramenta são também utilizados diferentes parametrizações. As estratégias de otimização propostas neste trabalho resolvem eficientemente problemas de otimização para a indústria de conformação metálica.

Exploring an uncommon host-parasite interaction to unveil the uniqueness of Perkinsus sp as a model organism

We report the exploration of some unique metabolic pathways in Perkinsus olseni a marine protist parasite, responsible to significant mortalities in mollusks, especially in bivalves all around the world. In Algarve, south of Portugal carpet shell clam Ruditapes decussatus mortalities can reach up to 70%, causing social and economic losses. The objective of studying those unique pathways, is finding new therapeutic strategies capable of controlling/eliminating P. olseni proliferation in clams. In that sense metabolic pathways, were explored, and drugs affecting these cycles were tested for activity. The first step involved the identification of the genes behind those pathways, the reconstitution of the main steps, and molecular characterization of those genes and later on, the identification of possible targets within the genes studied. Metabolic cycles were screened due to the fact of not being present in host or differ in a critical way, such as the following pathways: shikimate, MEP-­‐ isoprenoids, Leloir cycle for chitin production, purine biosynthesis (unique among protists), the de novo synthesis of folates (absent in metazoa) and some unique genes like, the alternative oxidase (a branch of respiratory chain) and the hypoxia sensor HPH. All those pathways were covered and possible chemical inhibition using therapeutic drugs was tested with positive results. The relation between the common host Ruditapes decussatus and P. olseni was also explored in a dimension not possible some years ago. With the accessibility to second generation sequencers and microarray analysis platforms, genes involved in host defense or parasite virulence and resistance to the host were deciphered, allowing aiming to new targets (mechanisms and pathways), offering new possibilities for the control of Perkinsus in close environments. The thousands of genes, generated by this work, sequenced and analyzed from this commercial valuable clam and for Perkinsus olseni will be an important and value tool for the scientific community, allowing a better understanding of host-­‐parasite interactions, promoting the usage of P. olseni as an emerging model for alveolata parasites.

Identification and molecular characterization of bone-related micrornas: functional implications

Tese de doutoramento, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2014

A Service Model For Delivering Care Closer to Home.

Upton Surgery (Worcestershire) has developed a flexible and responsive service model that facilitates multi-agency support for adult patients with complex care needs experiencing an acute health crisis. The purpose of this service is to provide appropriate interventions that avoid unnecessary hospital admissions or, alternatively, provide support to facilitate early discharge from secondary care. Key aspects of this service are the collaborative and proactive identification of patients at risk, rapid creation and deployment of a reactive multi-agency team and follow-up of patients with an appropriate long-term care plan. A small team of dedicated staff (the Complex Care Team) are pivotal to coordinating and delivering this service. Key skills are sophisticated leadership and project management skills, and these have been used sensitively to challenge some traditional roles and boundaries in the interests of providing effective, holistic care for the patient. This is a practical example of early implementation of the principles underlying the Department of Health’s (DH) recent Best Practice Guidance, ‘Delivering Care Closer to Home’ (DH, July 2008) and may provide useful learning points for other general practice surgeries considering implementing similar models. This integrated case management approach has had enthusiastic endorsement from patients and carers. In addition to the enhanced quality of care and experience for the patient, this approach has delivered value for money. Secondary care costs have been reduced by preventing admissions and also by reducing excess bed-days. The savings achieved have justified the ongoing commitment to the service and the staff employed in the Complex Care Team. The success of this service model has been endorsed recently by the ‘Customer Care’ award by ‘Management in Practice’. The Surgery was also awarded the ‘Practice of the Year’ award for this and a number of other customer-focussed projects.

Identification of Potential Sources of Airborne Olea Pollen in the Southwest Iberian Peninsula

This study aims to determine the potential origin of Olea pollen recorded in Badajoz in the Southwest of the Iberian Peninsula during 2009–2011. This was achieved using a combination of daily average and diurnal (hourly) airborne Olea pollen counts recorded at Badajoz (south-western Spain) and Évora (south-eastern Portugal), an inventory of olive groves in the studied area and air mass trajectory calculations computed using the HYSPLIT model. Examining olive pollen episodes at Badajoz that had distinctly different diurnal cycles in olive pollen in relation to the mean, allowed us to identify three different scenarios where olive pollen can be transported to the city from either distant or nearby sources during conditions with slow air mass movements. Back trajectory analysis showed that olive pollen can be transported to Badajoz from the West on prevailing winds, either directly or on slow moving air masses, and from high densities of olive groves situated to the Southeast (e.g. Andalucía). Regional scale transport of olive pollen can result in increased nighttime concentrations of this important aeroallergen. This could be particularly important in Mediterranean countries where people can be outdoors during this time due to climate and lifestyle. Such studies that examine sources and the atmospheric transport of pollen are valuable for allergy sufferers and health care professionals because the information can be incorporated into forecasts, the outputs of which are used for avoiding exposure to aeroallergens and planning medication. The results of studies of this nature can also be used for examining gene flow in this important agricultural crop.

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage