Host and Bacterial Proteins That Repress Recruitment of LC3 to Shigella Early during Infection

Shigella spp. are intracytosolic gram-negative pathogens that cause disease by invasion and spread through the colonic mucosa, utilizing host cytoskeletal components to form propulsive actin tails. We have previously identified the host factor Toca-1 as being recruited to intracellular S. flexneri and being required for efficient bacterial actin tail formation. We show that at early times during infection (40 min.), the type three-secreted effector protein IcsB recruits Toca-1 to intracellular bacteria and that recruitment of Toca-1 is associated with repression of recruitment of LC3, as well as with repression of recruitment of the autophagy marker NDP52, around these intracellular bacteria. LC3 is best characterized as a marker of autophagosomes, but also marks phagosomal membranes in the process LC3-associated phagocytosis. IcsB has previously been demonstrated to be required for S. flexneri evasion of autophagy at late times during infection (4–6 hr) by inhibiting binding of the autophagy protein Atg5 to the Shigella surface protein IcsA (VirG). Our results suggest that IcsB and Toca-1 modulation of LC3 recruitment restricts LC3-associated phagocytosis and/or LC3 recruitment to vacuolar membrane remnants. Together with published results, our findings suggest that IcsB inhibits innate immune responses in two distinct ways, first, by inhibiting LC3-associated phagocytosis and/or LC3 recruitment to vacuolar membrane remnants early during infection, and second, by inhibiting autophagy late during infection

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

Building robust prediction models for defective sensor data using Artificial Neural Networks

Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approach involves selecting a smaller set of relevant sensor signals using feature selection and using them to train a machine learning algorithm. However, this fails to address two prominent problems: (1) sensors are susceptible to failure when exposed to extreme conditions over a long periods of time; (2) sensors are electrical devices that can be affected by noise or electrical interference. Using the failed and noisy sensor signals as inputs largely reduce the prediction accuracy. To tackle this problem, it is advantageous to use the information from all sensor signals, so that the failure of one sensor can be compensated by another. In this work, we propose an Artificial Neural Network (ANN) based framework to exploit the information from a large number of signals. Secondly, our framework introduces a data augmentation approach to perform accurate predictions in spite of noisy signals. The plausibility of our framework is validated on real life industrial application from Robert Bosch GmbH.Comment: 16 pages, 7 figures. Currently under review. This research has obtained funding from the Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking, the framework programme for research and innovation Horizon 2020 (2014-2020) under grant agreement number 662189-MANTIS-2014-

Air ambulance flights in northern Norway 2002-2008. Increased number of secondary fixed wing (FW) operations and more use of rotor wing (RW) transports

Air ambulance service in Norway has been upgraded during the last years. European regulations concerning pilots’ working time and new treatment guidelines/strategies have called for more resources. The objective was to describe and analyse the two supplementary air ambulance [fixed wing (FW) and rotor wing (RW)] alternatives’ activity during the study period (2002-2008). Furthermore we aimed to compare our findings with reports from other north European regions. This is a retrospective analysis. The air ambulance fleet’s activity according to the electronic patient record database of “Luftambulansetjenesten ANS” (LABAS) was analysed. The subject was the fleet’s operations in northern Norway, logistics, and patients handled. Type of flight, distances, frequency, and patients served were the main outcome measures. A significant increase (45%) in the use of RW and a shift in FW operations (less primary and more secondary) were revealed. The shift in FW operations reflected the centralisation of several health care services [i.e. percutaneous cardiac intervention (PCI), trauma, and cancer surgery] during the study period. Cardiovascular disease (CVD) and injuries were the main diagnoses and constituted half of all operations. CVD was the most common cause of FW operations and injuries of the RW ones. The number of air ambulance operations was 16 per 1,000 inhabitants. This was more frequent than in other north European regions. The use of air ambulances and especially RW was significantly increased during the study period. The change in secondary FW operations reflected centralisation of medical care. When health care services are centralised, air ambulance services must be adjusted to the new settings

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

A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology

<p>Abstract</p> <p>Background</p> <p>Chest pain is the second most common chief complaint in North American emergency departments. Data from the U.S. suggest that 2.1% of patients with acute myocardial infarction and 2.3% of patients with unstable angina are misdiagnosed, with slightly higher rates reported in a recent Canadian study (4.6% and 6.4%, respectively). Information obtained from the history, 12-lead ECG, and a single set of cardiac enzymes is unable to identify patients who are safe for early discharge with sufficient sensitivity. The 2007 ACC/AHA guidelines for UA/NSTEMI do not identify patients at low risk for adverse cardiac events who can be safely discharged without provocative testing. As a result large numbers of low risk patients are triaged to chest pain observation units and undergo provocative testing, at significant cost to the healthcare system. Clinical decision rules use clinical findings (history, physical exam, test results) to suggest a diagnostic or therapeutic course of action. Currently no methodologically robust clinical decision rule identifies patients safe for early discharge.</p> <p>Methods/design</p> <p>The goal of this study is to derive a clinical decision rule which will allow emergency physicians to accurately identify patients with chest pain who are safe for early discharge. The study will utilize a prospective cohort design. Standardized clinical variables will be collected on all patients at least 25 years of age complaining of chest pain prior to provocative testing. Variables strongly associated with the composite outcome acute myocardial infarction, revascularization, or death will be further analyzed with multivariable analysis to derive the clinical rule. Specific aims are to: i) apply standardized clinical assessments to patients with chest pain, incorporating results of early cardiac testing; ii) determine the inter-observer reliability of the clinical information; iii) determine the statistical association between the clinical findings and the composite outcome; and iv) use multivariable analysis to derive a highly sensitive clinical decision rule to guide triage decisions.</p> <p>Discussion</p> <p>The study will derive a highly sensitive clinical decision rule to identify low risk patients safe for early discharge. This will improve patient care, lower healthcare costs, and enhance flow in our busy and overcrowded emergency departments.</p

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

Pretest probability assessment derived from attribute matching

BACKGROUND: Pretest probability (PTP) assessment plays a central role in diagnosis. This report compares a novel attribute-matching method to generate a PTP for acute coronary syndrome (ACS). We compare the new method with a validated logistic regression equation (LRE). METHODS: Eight clinical variables (attributes) were chosen by classification and regression tree analysis of a prospectively collected reference database of 14,796 emergency department (ED) patients evaluated for possible ACS. For attribute matching, a computer program identifies patients within the database who have the exact profile defined by clinician input of the eight attributes. The novel method was compared with the LRE for ability to produce PTP estimation <2% in a validation set of 8,120 patients evaluated for possible ACS and did not have ST segment elevation on ECG. 1,061 patients were excluded prior to validation analysis because of ST-segment elevation (713), missing data (77) or being lost to follow-up (271). RESULTS: In the validation set, attribute matching produced 267 unique PTP estimates [median PTP value 6%, 1(st)–3(rd )quartile 1–10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1(st)–3(rd )quartile 10–30%]. The areas under the receiver operating characteristic curves were 0.74 (95% CI 0.65 to 0.82) for the attribute matching curve and 0.68 (95% CI 0.62 to 0.77) for LRE. The attribute matching system categorized 1,670 (24%, 95% CI = 23–25%) patients as having a PTP < 2.0%; 28 developed ACS (1.7% 95% CI = 1.1–2.4%). The LRE categorized 244 (4%, 95% CI = 3–4%) with PTP < 2.0%; four developed ACS (1.6%, 95% CI = 0.4–4.1%). CONCLUSION: Attribute matching estimated a very low PTP for ACS in a significantly larger proportion of ED patients compared with a validated LRE