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

Simultaneous Detection of CDC Category “A” DNA and RNA Bioterrorism Agents by Use of Multiplex PCR & RT-PCR Enzyme Hybridization Assays

Assays to simultaneously detect multiple potential agents of bioterrorism are limited. Two multiplex PCR and RT-PCR enzyme hybridization assays (mPCR-EHA, mRT-PCR-EHA) were developed to simultaneously detect many of the CDC category “A” bioterrorism agents. The “Bio T” DNA assay was developed to detect: Variola major (VM), Bacillus anthracis (BA), Yersinia pestis (YP), Francisella tularensis (FT) and Varicella zoster virus (VZV). The “Bio T” RNA assay (mRT-PCR-EHA) was developed to detect: Ebola virus (Ebola), Lassa fever virus (Lassa), Rift Valley fever (RVF), Hantavirus Sin Nombre species (HSN) and dengue virus (serotypes 1–4). Sensitivity and specificity of the 2 assays were tested by using genomic DNA, recombinant plasmid positive controls, RNA transcripts controls, surrogate (spiked) clinical samples and common respiratory pathogens. The analytical sensitivity (limit of detection (LOD)) of the DNA asssay for genomic DNA was 1×100∼1×102 copies/mL for BA, FT and YP. The LOD for VZV whole organism was 1×10−2 TCID50/mL. The LOD for recombinant controls ranged from 1×102∼1×103copies/mL for BA, FT, YP and VM. The RNA assay demonstrated LOD for RNA transcript controls of 1×104∼1×106 copies/mL without extraction and 1×105∼1×106 copies/mL with extraction for Ebola, RVF, Lassa and HSN. The LOD for dengue whole organisms was ∼1×10−4 dilution for dengue 1 and 2, 1×104 LD50/mL and 1×102 LD50/mL for dengue 3 and 4. The LOD without extraction for recombinant plasmid DNA controls was ∼1×103 copies/mL (1.5 input copies/reaction) for Ebola, RVF, Lassa and HSN. No cross-reactivity of primers and probes used in both assays was detected with common respiratory pathogens or between targeted analytes. Clinical sensitivity was estimated using 264 surrogate clinical samples tested with the BioT DNA assay and 549 samples tested with the BioT RNA assay. The clinical specificity is 99.6% and 99.8% for BioT DNA assay and BioT RNA assay, respectively. The surrogate sensitivities of these two assays were 100% (95%CI 83–100) for FT, BA (pX02), YP, VM, VZV, dengue 2,3,4 and 95% (95%CI 75–100) for BA (pX01) and dengue 1 using spiked clinical specimens. The specificity of both BioT multiplex assays on spiked specimens was 100% (95% CI 99–100). Compared to other available assays (culture, serology, PCR, etc.) both the BioT DNA mPCR-EHA and BioT RNA mRT-PCR-EHA are rapid, sensitive and specific assays for detecting many category “A” Bioterrorism agents using a standard thermocycler

Biophysical characterisation of human LincRNA-p21 sense and antisense Alu inverted repeats

Open access article. Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0) appliesHuman Long Intergenic Noncoding RNA-p21 (LincRNA-p21) is a regulatory noncoding RNA that plays an important role in promoting apoptosis. LincRNA-p21 is also critical in down-regulating many p53 target genes through its interaction with a p53 repressive complex. The interaction between LincRNA-p21 and the repressive complex is likely dependent on the RNA tertiary structure. Previous studies have determined the two-dimensional secondary structures of the sense and antisense human LincRNA-p21 AluSx1 IRs using SHAPE. However, there were no insights into its three-dimensional structure. Therefore, we in vitro transcribed the sense and antisense regions of LincRNA-p21 AluSx1 Inverted Repeats (IRs) and performed analytical ultracentrifugation, size exclusion chromatography, light scattering, and small angle X-ray scattering (SAXS) studies. Based on these studies, we determined low-resolution, three-dimensional structures of sense and antisense LincRNA-p21. By adapting previously known two-dimensional information, we calculated their sense and antisense high-resolution models and determined that they agree with the low-resolution structures determined using SAXS. Thus, our integrated approach provides insights into the structure of LincRNA-p21 Alu IRs. Our study also offers a viable pipeline for combining the secondary structure information with biophysical and computational studies to obtain high-resolution atomistic models for long noncoding RNAs.Ye

Policy challenges for the pediatric rheumatology workforce: Part III. the international situation

Survival dominates current pediatric global health priorities. Diseases of poverty largely contribute to overall mortality in children under 5 years of age. Infectious diseases and injuries account for 75% of cause-specific mortality among children ages 5-14 years. Twenty percent of the world's population lives in extreme poverty (income below US $1.25/day). Within this population, essential services and basic needs are not met, including clean water, sanitation, adequate nutrition, shelter, access to health care, medicines and education. In this context, musculoskeletal disease comprises 0.1% of all-cause mortality in children ages 5-14 years. Worldwide morbidity from musculoskeletal disease remains generally unknown in the pediatric age group. This epidemiologic data is not routinely surveyed by international agencies, including the World Health Organization. The prevalence of pediatric rheumatic diseases based on data from developed nations is in the range of 2,500 - 3,000 cases per million children. Developing countries' needs for musculoskeletal morbidity are undergoing an epidemiologic shift to chronic conditions, as leading causes of pediatric mortality are slowly quelled

Epidemiologic Observations from Passive and Targeted Surveillance during the First Wave of the 2009 H1N1 Influenza Pandemic in Milwaukee, WI

The first wave of the 2009 influenza H1N1 pandemic (H1N1pdm) in Milwaukee, WI has been recognized as the largest reported regional outbreak in the United States. The epidemiologic and clinical characteristics of this large first wave outbreak from April 28th 2009–July 25th 2009, studied using both passive and targeted surveillance methodologies are presented. A total of 2791 individuals with H1N1pdm infection were identified; 60 % were 5–18 years old. The 5–18 year and 0–4 year age groups had high infection (1131 and 1101 per 100,000) and hospitalization (49 and 12 per 100,000) rates respectively. Non-Hispanic blacks and Hispanics had the highest hospitalization and infection rates. In targeted surveillance, infected patients had fever (78%), cough (80%), sore throat (38%), and vomiting or diarrhea (8%). The “influenza like illness” definition captured only 68 % of infected patients. Modeling estimates that 10.3 % of Milwaukee population was infected in the first wave and 59% were asymptomatic. The distinct epidemiologic profile of H1N1pdm infections observed in the study has direct implications for predicting the burden of infection and hospitalization in the next waves of H1N1pdm. Careful consideration of demographic predictors of infection and hospitalization with H1N1pdm will be important for effective preparedness for subsequent influenza seasons

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs

Introduction of a Novel Swine-Origin Influenza A (H1N1) Virus into Milwaukee, Wisconsin in 2009

On 17 April 2009, novel swine origin influenza A virus (S-OIV) cases appeared within the United States. Most influenza A diagnostic assays currently utilized in local clinical laboratories do not allow definitive subtype determination. Detailed subtype analysis of influenza A positive samples in our laboratory allowed early confirmation of a large outbreak of S-OIV in southeastern Wisconsin (SEW). The initial case of S-OIV in SEW was detected on 28 April 2009. All influenza A samples obtained during the 16 week period prior to 28 April 2009, and the first four weeks of the subsequent epidemic were sub typed. Four different multiplex assays were employed, utilizing real time PCR and end point PCR to fully subtype human and animal influenza viral components. Specific detection of S-OIV was developed within days. Data regarding patient demographics and other concurrently circulating viruses were analyzed. During the first four weeks of the epidemic, 679 of 3726 (18.2%) adults and children tested for influenza A were identified with S-OIV infection. Thirteen patients (0.34%) tested positive for seasonal human subtypes of influenza A during the first two weeks and none in the subsequent 2 weeks of the epidemic. Parainfluenza viruses were the most prevalent seasonal viral agents circulating during the epidemic (of those tested), with detection rates of 12% followed by influenza B and RSV at 1.9% and 0.9% respectively. S-OIV was confirmed on day 2 of instituting subtype testing and within 4 days of report of national cases of S-OIV. Novel surge capacity diagnostic infrastructure exists in many specialty and research laboratories around the world. The capacity for broader influenza A sub typing at the local laboratory level allows timely and accurate detection of novel strains as they emerge in the community, despite the presence of other circulating viruses producing identical illness. This is likely to become increasingly important given the need for appropriate subtype driven anti-viral therapy and the potential shortage of such medications in a large epidemic

Whole Genome Sequencing and Evolutionary Analysis of Human Respiratory Syncytial Virus A and B from Milwaukee, WI 1998-2010

BACKGROUND: Respiratory Syncytial Virus (RSV) is the leading cause of lower respiratory-tract infections in infants and young children worldwide. Despite this, only six complete genome sequences of original strains have been previously published, the most recent of which dates back 35 and 26 years for RSV group A and group B respectively. METHODOLOGY/PRINCIPAL FINDINGS: We present a semi-automated sequencing method allowing for the sequencing of four RSV whole genomes simultaneously. We were able to sequence the complete coding sequences of 13 RSV A and 4 RSV B strains from Milwaukee collected from 1998-2010. Another 12 RSV A and 5 RSV B strains sequenced in this study cover the majority of the genome. All RSV A and RSV B sequences were analyzed by neighbor-joining, maximum parsimony and Bayesian phylogeny methods. Genetic diversity was high among RSV A viruses in Milwaukee including the circulation of multiple genotypes (GA1, GA2, GA5, GA7) with GA2 persisting throughout the 13 years of the study. However, RSV B genomes showed little variation with all belonging to the BA genotype. For RSV A, the same evolutionary patterns and clades were seen consistently across the whole genome including all intergenic, coding, and non-coding regions sequences. CONCLUSIONS/SIGNIFICANCE: The sequencing strategy presented in this work allows for RSV A and B genomes to be sequenced simultaneously in two working days and with a low cost. We have significantly increased the amount of genomic data that is available for both RSV A and B, providing the basic molecular characteristics of RSV strains circulating in Milwaukee over the last 13 years. This information can be used for comparative analysis with strains circulating in other communities around the world which should also help with the development of new strategies for control of RSV, specifically vaccine development and improvement of RSV diagnostics