EMOCS: evolutionary multi-objective optimisation for clinical scorecard generation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordClinical scorecards of risk factors associated with disease severity or mortality outcome are used by clinicians to make treatment decisions and optimize resources. This study develops an automated tool or framework based on evolutionary algorithms for the derivation of scorecards from clinical data. The techniques employed are based on the NSGA-II Multi-objective Optimization Genetic Algorithm (GA) which optimizes the Pareto-front of two clinically-relevant scorecard objectives, size and accuracy. Three automated methods are presented which improve on previous manually derived scorecards. The first is a hybrid algorithm which uses the GA for feature selection and a decision tree for scorecard generation. In the second, the GA generates the full scorecard. The third is an extended full scoring system in which the GA also generates the scorecard scores. In this system combinations of features and thresholds for each scorecard point are selected by the algorithm and the evolutionary process is used to discover near-optimal Pareto-fronts of scorecards for exploration by expert decision makers. This is shown to produce scorecards that improve upon a human derived example for C.Difficile, an important infection found globally in communities and hospitals, although the methods described are applicable to any disease where the required data is available.Engineering and Physical Sciences Research Council (EPSRC

Differential spatial repositioning of activated genes in Biomphalaria glabrata snails infected with Schistosoma mansoni

Copyright @ 2014 Arican-Goktas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.Schistosomiasis is an infectious disease infecting mammals as the definitive host and fresh water snails as the intermediate host. Understanding the molecular and biochemical relationship between the causative schistosome parasite and its hosts will be key to understanding and ultimately treating and/or eradicating the disease. There is increasing evidence that pathogens that have co-evolved with their hosts can manipulate their hosts' behaviour at various levels to augment an infection. Bacteria, for example, can induce beneficial chromatin remodelling of the host genome. We have previously shown in vitro that Biomphalaria glabrata embryonic cells co-cultured with schistosome miracidia display genes changing their nuclear location and becoming up-regulated. This also happens in vivo in live intact snails, where early exposure to miracidia also elicits non-random repositioning of genes. We reveal differences in the nuclear repositioning between the response of parasite susceptible snails as compared to resistant snails and with normal or live, attenuated parasites. Interestingly, the stress response gene heat shock protein (Hsp) 70 is only repositioned and then up-regulated in susceptible snails with the normal parasite. This movement and change in gene expression seems to be controlled by the parasite. Other differences in the behaviour of genes support the view that some genes are responding to tissue damage, for example the ferritin genes move and are up-regulated whether the snails are either susceptible or resistant and upon exposure to either normal or attenuated parasite. This is the first time host genome reorganisation has been seen in a parasitic host and only the second time for any pathogen. We believe that the parasite elicits a spatio-epigenetic reorganisation of the host genome to induce favourable gene expression for itself and this might represent a fundamental mechanism present in the human host infected with schistosome cercariae as well as in other host-pathogen relationships.NIH and Sandler Borroughs Wellcome Travel Fellowshi

Challenges in the assessment of medical devices: the MedtecHTA project

Assessing medical devices (MDs) raises challenges which require us to reflect on whether current methods are adequate. Major features of devices are: (i) device–operator interaction can generate learning curve effects; (ii) incremental nature of innovation needs to be addressed by careful identification of the alternatives for comparative and incremental costeffectiveness analysis; and (iii) broader organizational impact in terms of training and infrastructure, coupled with dynamic pricing, requires a more flexible approach to costing. The objective of the MedtecHTA project was to investigate improvements in HTA methods to allow for more comprehensive evaluation of MDs. It consisted of several work packages concerning (i) the available evidence on the currently adopted approaches for regulation and HTA of medical devices; (ii) the geographical variation in access to MDs; (iii) the development of methodological frameworks for conducting comparative effectiveness research and economic evaluation of MDs; and (iv) the organizational impact of MDs. This introductory paper summarizes the main results of the project and draws out the main overarching themes. This supplement represents a comprehensive report of all the main findings of the MedtecHTA project, and it is intended to be the main source for researchers and policy makers wanting information on the project

Calculating the potential for within-flight transmission of influenza A (H1N1)

Abstract Background Clearly air travel, by transporting infectious individuals from one geographic location to another, significantly affects the rate of spread of influenza A (H1N1). However, the possibility of within-flight transmission of H1N1 has not been evaluated; although it is known that smallpox, measles, tuberculosis, SARS and seasonal influenza can be transmitted during commercial flights. Here we present the first quantitative risk assessment to assess the potential for within-flight transmission of H1N1. Methods We model airborne transmission of infectious viral particles of H1N1 within a Boeing 747 using methodology from the field of quantitative microbial risk assessment. Results The risk of catching H1N1 will essentially be confined to passengers travelling in the same cabin as the source case. Not surprisingly, we find that the longer the flight the greater the number of infections that can be expected. We calculate that H1N1, even during long flights, poses a low to moderate within-flight transmission risk if the source case travels First Class. Specifically, 0-1 infections could occur during a 5 hour flight, 1-3 during an 11 hour flight and 2-5 during a 17 hour flight. However, within-flight transmission could be significant, particularly during long flights, if the source case travels in Economy Class. Specifically, two to five infections could occur during a 5 hour flight, 5-10 during an 11 hour flight and 7-17 during a 17 hour flight. If the aircraft is only partially loaded, under certain conditions more infections could occur in First Class than in Economy Class. During a 17 hour flight, a greater number of infections would occur in First Class than in Economy if the First Class Cabin is fully occupied, but Economy class is less than 30% full. Conclusions Our results provide insights into the potential utility of air travel restrictions on controlling influenza pandemics in the winter of 2009/2010. They show travel by one infectious individual, rather than causing a single outbreak of H1N1, could cause several simultaneous outbreaks. These results imply that, during a pandemic, quarantining passengers who travel in Economy on long-haul flights could potentially be an important control strategy. Notably, our results show that quarantining passengers who travel First Class would be unlikely to be an effective control strategy

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens

P73 regulates cisplatin-induced apoptosis in ovarian cancer cells via a calcium/calpain-dependent mechanism

P73 is important in drug-induced apoptosis in some cancer cells, yet its role in the regulation of chemosensitivity in ovarian cancer (OVCA) is poorly understood. Furthermore, if and how the deregulation of p73-mediated apoptosis confers resistance to cisplatin (CDDP) treatment is unclear. Here we demonstrate that TAp73α over-expression enhanced CDDP-induced PARP cleavage and apoptosis in both chemosensitive (OV2008 and A2780s) and their resistant counterparts (C13* and A2780cp) and another chemoresistant OVCA cells (Hey); in contrast, the effect of ΔNp73α over-expression was variable. P73α downregulation attenuated CDDP-induced PUMA and NOXA upregulation and apoptosis in OV2008 cells. CDDP decreased p73α steady-state protein levels in OV2008, but not in C13*, although the mRNA expression was identical. CDDP-induced p73α downregulation was mediated by a calpain-dependent pathway. CDDP induced calpain activation and enhanced its cytoplasmic interaction and co-localization with p73α in OV2008, but not C13* cells. CDDP increased the intracellular calcium concentration ([Ca2+]i) in OV2008 but not C13* whereas cyclopiazonic acid (CPA), a Ca2+-ATPase inhibitor, caused this response and calpain activation, p73α processing and apoptosis in both cell types. CDDP-induced [Ca2+]i increase in OV2008 cells was not effected by the elimination of extracellular Ca2+, but this was attenuated by the depletion of internal Ca2+ store, indicating that mobilization of intracellular Ca2+] stores was potentially involved. These findings demonstrate that p73α and its regulation by the Ca2+-mediated calpain pathway are involved in CDDP-induced apoptosis in OVCA cells and that dysregulation of Ca2+/calpain/p73 signaling may in part be the pathophysiology of CDDP resistance. Understanding the cellular and molecular mechanisms of chemoresistance will direct the development of effective strategies for the treatment of chemoresistant OVCA

Identifying Currents in the Gene Pool for Bacterial Populations Using an Integrative Approach

The evolution of bacterial populations has recently become considerably better understood due to large-scale sequencing of population samples. It has become clear that DNA sequences from a multitude of genes, as well as a broad sample coverage of a target population, are needed to obtain a relatively unbiased view of its genetic structure and the patterns of ancestry connected to the strains. However, the traditional statistical methods for evolutionary inference, such as phylogenetic analysis, are associated with several difficulties under such an extensive sampling scenario, in particular when a considerable amount of recombination is anticipated to have taken place. To meet the needs of large-scale analyses of population structure for bacteria, we introduce here several statistical tools for the detection and representation of recombination between populations. Also, we introduce a model-based description of the shape of a population in sequence space, in terms of its molecular variability and affinity towards other populations. Extensive real data from the genus Neisseria are utilized to demonstrate the potential of an approach where these population genetic tools are combined with an phylogenetic analysis. The statistical tools introduced here are freely available in BAPS 5.2 software, which can be downloaded from http://web.abo.fi/fak/mnf/mate/jc/software/baps.html

Work, life and time in the new economy: an introduction

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