Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

BACKGROUND: The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS: Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ( unaided ), versus after use of the DDSS ( aided ). RESULTS: The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p \u3c 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an open book approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS: These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists\u27 ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02205086

MultiSig: a new high-precision approach to the analysis of complex biomolecular systems

MultiSig is a newly developed mode of analysis of sedimentation equilibrium (SE) experiments in the analytical ultracentrifuge, having the capability of taking advantage of the remarkable precision (~0.1 % of signal) of the principal optical (fringe) system employed, thus supplanting existing methods of analysis through reducing the ‘noise’ level of certain important parameter estimates by up to orders of magnitude. Long-known limitations of the SE method, arising from lack of knowledge of the true fringe number in fringe optics and from the use of unstable numerical algorithms such as numerical differentiation, have been transcended. An approach to data analysis, akin to ‘spatial filtering’, has been developed, and shown by both simulation and practical application to be a powerful aid to the precision with which near-monodisperse systems can be analysed, potentially yielding information on protein-solvent interaction. For oligo- and poly-disperse systems the information returned includes precise average mass distributions over both cell radial and concentration ranges and mass-frequency histograms at fixed radial positions. The application of MultiSig analysis to various complex heterogenous systems and potentially multiply-interacting carbohydrate oligomers is described

Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user

Analysis of fire throttling in longitudinally ventilated tunnels with a one-dimensional model

Fire throttling is the increase in flow resistance due to a large fire in a longitudinally ventilated tunnel. Although the fire throttling effect has been been known and studied for tunnels over the last 40 years, there is not yet a consistent one-dimensional (1D) model that can describe this behaviour or a framework suitable for practical application. We propose a semi-empirical model, based upon pipe flow engineering principles, to describe this effect by separating the resistance to flow, or pressure loses in three parts: upstream of the fire, locally at the fire, and downstream of the fire. The proposed 1D model called TE1D is derived from a simple steady one-dimensional momentum balance in which a semi-empirical mean temperature distribution is assumed across the tunnel. We verify the model by comparing the pressures losses it predicts with those calculated in CFD simulations based on OpenFOAM and Fire Dynamics Simulator. The comparison shows good agreement between the CFD codes for the range of fires sizes considered from 5 to 50 MW and good agreement between TE1D and the CFD results with the proposed 1D model for fire sizes below 30 MW. However, for values above there are large discrepancies between the results obtained by the TE1D and CFD. We posit as a potential explanation that these differences are due to flow and temperature stratification which is not accounted for in the 1D model. The model using pipe flow principles allows engineers to adopt this model for design, together with other pressure losses considered in tunnel ventilation

Challenges and recent progress in drug discovery for tropical diseases

Infectious tropical diseases have a huge effect in terms of mortality and morbidity, and impose a heavy economic burden on affected countries. These diseases predominantly affect the world’s poorest people. Currently available drugs are inadequate for the majority of these diseases, and there is an urgent need for new treatments. This Review discusses some of the challenges involved in developing new drugs to treat these diseases and highlights recent progress. While there have been notable successes, there is still a long way to go.</p

The effectiveness of e-&amp; mHealth interventions to promote physical activity and healthy diets in developing countries: a systematic review

Background: Promoting physical activity and healthy eating is important to combat the unprecedented rise in NCDs in many developing countries. Using modern information-and communication technologies to deliver physical activity and diet interventions is particularly promising considering the increased proliferation of such technologies in many developing countries. The objective of this systematic review is to investigate the effectiveness of e-&amp; mHealth interventions to promote physical activity and healthy diets in developing countries.Methods: Major databases and grey literature sources were searched to retrieve studies that quantitatively examined the effectiveness of e-&amp; mHealth interventions on physical activity and diet outcomes in developing countries. Additional studies were retrieved through citation alerts and scientific social media allowing study inclusion until August 2016. The CONSORT checklist was used to assess the risk of bias of the included studies.Results: A total of 15 studies conducted in 13 developing countries in Europe, Africa, Latin-and South America and Asia were included in the review. The majority of studies enrolled adults who were healthy or at risk of diabetes or hypertension. The average intervention length was 6.4 months, and text messages and the Internet were the most frequently used intervention delivery channels. Risk of bias across the studies was moderate (55.7 % of the criteria fulfilled). Eleven studies reported significant positive effects of an e-&amp; mHealth intervention on physical activity and/or diet behaviour. Respectively, 50 % and 70 % of the interventions were effective in promoting physical activity and healthy diets.Conclusions: The majority of studies demonstrated that e-&amp; mHealth interventions were effective in promoting physical activity and healthy diets in developing countries. Future interventions should use more rigorous study designs, investigate the cost-effectiveness and reach of interventions, and focus on emerging technologies, such as smart phone apps and wearable activity trackers.Trial registration: The review protocol can be retrieved from the PROSPERO database (Registration ID: CRD42015029240)

Longitudinal seroepidemiologic study of the 2009 pandemic influenza A (H1N1) infection among health care workers in a children's hospital

<p>Abstract</p> <p>Background</p> <p>To probe seroepidemiology of the 2009 pandemic influenza A (H1N1) among health care workers (HCWs) in a children's hospital.</p> <p>Methods</p> <p>From August 2009 to March 2010, serum samples were drawn from 150 HCWs in a children's hospital in Taipei before the 2009 influenza A (H1N1) pandemic, before H1N1 vaccination, and after the pandemic. HCWs who had come into direct contact with 2009 influenza A (H1N1) patients or their clinical respiratory samples during their daily work were designated as a high-risk group. Antibody levels were determined by hemagglutination inhibition (HAI) assay. A four-fold or greater increase in HAI titers between any successive paired sera was defined as seroconversion, and factors associated with seroconversion were analyzed.</p> <p>Results</p> <p>Among the 150 HCWs, 18 (12.0%) showed either virological or serological evidence of 2009 pandemic influenza A (H1N1) infection. Of the 90 unvaccinated HCWs, baseline and post-pandemic seroprotective rates were 5.6% and 20.0%. Seroconversion rates among unvaccinated HCWs were 14.4% (13/90), 22.5% (9/40), and 8.0% (4/50) for total, high-risk group, and low-risk group, respectively. Multivariate analysis revealed being in the high-risk group is an independent risk factor associated with seroconversion.</p> <p>Conclusion</p> <p>The infection rate of 2009 pandemic influenza A (H1N1) in HCWs was moderate and not higher than that for the general population. The majority of unvaccinated HCWs remained susceptible. Direct contact of influenza patients and their respiratory samples increased the risk of infection.</p

Hydrogen Sulfide and Neurogenic Inflammation in Polymicrobial Sepsis: Involvement of Substance P and ERK-NF-κB Signaling

Hydrogen sulfide (H2S) has been shown to induce transient receptor potential vanilloid 1 (TRPV1)-mediated neurogenic inflammation in polymicrobial sepsis. However, endogenous neural factors that modulate this event and the molecular mechanism by which this occurs remain unclear. Therefore, this study tested the hypothesis that whether substance P (SP) is one important neural element that implicates in H2S-induced neurogenic inflammation in sepsis in a TRPV1-dependent manner, and if so, whether H2S regulates this response through activation of the extracellular signal-regulated kinase-nuclear factor-κB (ERK-NF-κB) pathway. Male Swiss mice were subjected to cecal ligation and puncture (CLP)-induced sepsis and treated with TRPV1 antagonist capsazepine 30 minutes before CLP. DL-propargylglycine (PAG), an inhibitor of H2S formation, was administrated 1 hour before or 1 hour after sepsis, whereas sodium hydrosulfide (NaHS), an H2S donor, was given at the same time as CLP. Capsazepine significantly attenuated H2S-induced SP production, inflammatory cytokines, chemokines, and adhesion molecules levels, and protected against lung and liver dysfunction in sepsis. In the absence of H2S, capsazepine caused no significant changes to the PAG-mediated attenuation of lung and plasma SP levels, sepsis-associated systemic inflammatory response and multiple organ dysfunction. In addition, capsazepine greatly inhibited phosphorylation of ERK1/2 and inhibitory κBα, concurrent with suppression of NF-κB activation even in the presence of NaHS. Furthermore, capsazepine had no effect on PAG-mediated abrogation of these levels in sepsis. Taken together, the present findings show that H2S regulates TRPV1-mediated neurogenic inflammation in polymicrobial sepsis through enhancement of SP production and activation of the ERK-NF-κB pathway

Hospitalized adult patients with 2009 influenza A(H1N1) in Beijing, China: risk factors for hospital mortality

<p>Abstract</p> <p>Background</p> <p>In April 2009, the pandemic influenza A(H1N1) virus emerged and spread globally. The objective of this study was to describe the independent risk factors for hospital mortality and the treatment effect of corticosteroids among patients with 2009 influenza A(H1N1) infection.</p> <p>Methods</p> <p>We retrospectively obtained clinical data of 155 adult patients with confirmed infection of 2009 influenza A(H1N1) in 23 hospitals in Beijing, China from October 1 to December 23, 2009. Risk factors for hospital mortality were identified with multivariate logistic regression analysis.</p> <p>Results</p> <p>Among the 155 patients, 90 (58.1%) were male, and mean age was 43.0 ± 18.6 years, and comorbidities were present in 81 (52.3%) patients. The most common organ dysfunctions included acute respiratory failure, altered mental status, septic shock, and acute renal failure. Oseltamivir was initiated in 125 patients (80.6%), only 16 patients received antiviral therapy within 48 hours after symptom onset. Fifty-two patients (33.5%) were treated with systemic corticosteroids, with a median daily dose of 80 mg. Twenty-seven patients (17.4%) died during hospital stay. Diabetes [odds ratio (OR) 8.830, 95% confidence interval [CI] 2.041 to 38.201, p = 0.004) and lactate dehydrogenase (LDH) level (OR 1.240, 95% CI 1.025 to 1.500, p = 0.027) were independent risk factors of hospital death, as were septic shock and altered mental status. Corticosteroids use was associated with a trend toward higher hospital mortality (OR 3.668, 95% CI 0.987 to 13.640, p = 0.052).</p> <p>Conclusions</p> <p>Hospitalized patients with 2009 H1N1 influenza had relative poor outcome. The risk factors at hospitalization may help clinicians to identify the high-risk patients. In addition, corticosteroids use should not be regarded as routine pharmacologic therapy.</p