Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data

BACKGROUND: In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings. METHODS: 1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests. RESULTS: ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model. CONCLUSIONS: ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population

Implementing Electronic Tablet-Based Education of Acute Care Patients

Poor education-related discharge preparedness for patients with heart failure is believed to be a major cause of avoidable rehospitalizations. Technology-based applications offer innovative educational approaches that may improve educational readiness for patients in both inpatient and outpatient settings; however, a number of challenges exist when implementing electronic devices in the clinical setting. Implementation challenges include processes for "on-boarding" staff, mediating risks of cross-contamination with patients' device use, and selling the value to staff and health system leaders to secure the investment in software, hardware, and system support infrastructure. Strategies to address these challenges are poorly described in the literature. The purpose of this article is to present a staff development program designed to overcome challenges in implementing an electronic, tablet-based education program for patients with heart failure

Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence - by controlling for phylogenetic structure - for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease

Theoretical and technological building blocks for an innovation accelerator

The scientific system that we use today was devised centuries ago and is inadequate for our current ICT-based society: the peer review system encourages conservatism, journal publications are monolithic and slow, data is often not available to other scientists, and the independent validation of results is limited. Building on the Innovation Accelerator paper by Helbing and Balietti (2011) this paper takes the initial global vision and reviews the theoretical and technological building blocks that can be used for implementing an innovation (in first place: science) accelerator platform driven by re-imagining the science system. The envisioned platform would rest on four pillars: (i) Redesign the incentive scheme to reduce behavior such as conservatism, herding and hyping; (ii) Advance scientific publications by breaking up the monolithic paper unit and introducing other building blocks such as data, tools, experiment workflows, resources; (iii) Use machine readable semantics for publications, debate structures, provenance etc. in order to include the computer as a partner in the scientific process, and (iv) Build an online platform for collaboration, including a network of trust and reputation among the different types of stakeholders in the scientific system: scientists, educators, funding agencies, policy makers, students and industrial innovators among others. Any such improvements to the scientific system must support the entire scientific process (unlike current tools that chop up the scientific process into disconnected pieces), must facilitate and encourage collaboration and interdisciplinarity (again unlike current tools), must facilitate the inclusion of intelligent computing in the scientific process, must facilitate not only the core scientific process, but also accommodate other stakeholders such science policy makers, industrial innovators, and the general public

A Proteomic and Cellular Analysis of Uropods in the Pathogen Entamoeba histolytica

Exposure of Entamoeba histolytica to specific ligands induces cell polarization via the activation of signalling pathways and cytoskeletal elements. The process leads to formation of a protruding pseudopod at the front of the cell and a retracting uropod at the rear. In the present study, we show that the uropod forms during the exposure of trophozoites to serum isolated from humans suffering of amoebiasis. To investigate uropod assembly, we used LC-MS/MS technology to identify protein components in isolated uropod fractions. The galactose/N-acetylgalactosamine lectin, the immunodominant antigen M17 (which is specifically recognized by serum from amoeba-infected persons) and a few other cells adhesion-related molecules were primarily involved. Actin-rich cytoskeleton components, GTPases from the Rac and Rab families, filamin, α-actinin and a newly identified ezrin-moesin-radixin protein were the main factors found to potentially interact with capped receptors. A set of specific cysteine proteases and a serine protease were enriched in isolated uropod fractions. However, biological assays indicated that cysteine proteases are not involved in uropod formation in E. histolytica, a fact in contrast to the situation in human motile immune cells. The surface proteins identified here are testable biomarkers which may be either recognized by the immune system and/or released into the circulation during amoebiasis

Small RNAs with 5′-Polyphosphate Termini Associate with a Piwi-Related Protein and Regulate Gene Expression in the Single-Celled Eukaryote Entamoeba histolytica

Small interfering RNAs regulate gene expression in diverse biological processes, including heterochromatin formation and DNA elimination, developmental regulation, and cell differentiation. In the single-celled eukaryote Entamoeba histolytica, we have identified a population of small RNAs of 27 nt size that (i) have 5′-polyphosphate termini, (ii) map antisense to genes, and (iii) associate with an E. histolytica Piwi-related protein. Whole genome microarray expression analysis revealed that essentially all genes to which antisense small RNAs map were not expressed under trophozoite conditions, the parasite stage from which the small RNAs were cloned. However, a number of these genes were expressed in other E. histolytica strains with an inverse correlation between small RNA and gene expression level, suggesting that these small RNAs mediate silencing of the cognate gene. Overall, our results demonstrate that E. histolytica has an abundant 27 nt small RNA population, with features similar to secondary siRNAs from C. elegans, and which appear to regulate gene expression. These data indicate that a silencing pathway mediated by 5′-polyphosphate siRNAs extends to single-celled eukaryotic organisms

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data

Intermediate filaments enable pathogen docking to trigger type 3 effector translocation

Type 3 secretion systems (T3SSs) of bacterial pathogens translocate bacterial effector proteins that mediate disease into the eukaryotic cytosol. Effectors traverse the plasma membrane through a translocon pore formed by T3SS proteins. In a genome-wide selection, we identified the intermediate filament vimentin as required for infection by the T3SS-dependent pathogen Shigella flexneri. We found that vimentin is required for efficient T3SS translocation of effectors by S. flexneri and other pathogens that use T3SS, Salmonella Typhimurium and Yersinia pseudotuberculosis. Vimentin and the intestinal epithelial intermediate filament keratin 18 interact with the C-terminus of the Shigella translocon pore protein IpaC. Vimentin and its interaction with IpaC are dispensable for pore formation, but are required for stable docking of S. flexneri to cells; moreover, stable docking triggers effector secretion. These findings establish that stable docking of the bacterium specifically requires intermediate filaments, is a process distinct from pore formation, and is a prerequisite for effector secretion

Structure Analysis of Entamoeba histolytica DNMT2 (EhMeth)

In eukaryotes, DNA methylation is an important epigenetic modification that is generally involved in gene regulation. Methyltransferases (MTases) of the DNMT2 family have been shown to have a dual substrate specificity acting on DNA as well as on three specific tRNAs (tRNAAsp, tRNAVal, tRNAGly). Entamoeba histolytica is a major human pathogen, and expresses a single DNA MTase (EhMeth) that belongs to the DNMT2 family and shows high homology to the human enzyme as well as to the bacterial DNA MTase M.HhaI. The molecular basis for the recognition of the substrate tRNAs and discrimination of non-cognate tRNAs is unknown. Here we present the crystal structure of the cytosine-5-methyltransferase EhMeth at a resolution of 2.15 Å, in complex with its reaction product S-adenosyl-L-homocysteine, revealing all parts of a DNMT2 MTase, including the active site loop. Mobility shift assays show that in vitro the full length tRNA is required for stable complex formation with EhMeth