Profiling dizziness in older primary care patients: an empirical study

Background

The diagnostic approach to dizzy, older patients is not straightforward as many organ systems can be involved and evidence for diagnostic strategies is lacking. A first differentiation in diagnostic subtypes or profiles may guide the diagnostic process of dizziness and can serve as a classification system in future research. In the literature this has been done, but based on pathophysiological reasoning only.

Objective

To establish a classification of diagnostic profiles of dizziness based on empirical data.

Design

Cross-sectional study.

Participants and Setting

417 consecutive patients of 65 years and older presenting with dizziness to 45 primary care physicians in the Netherlands from July 2006 to January 2008.

Methods

We performed tests, including patient history, and physical and additional examination, previously selected by an international expert panel and based on an earlier systematic review. We used the results of these tests in a principal component analysis for exploration, data-reduction and finally differentiation into diagnostic dizziness profiles.

Results

Demographic data and the results of the tests yielded 221 variables, of which 49 contributed to the classification of dizziness into six diagnostic profiles, that may be named as follows: “frailty”, “psychological”, “cardiovascular”, “presyncope”, “non-specific dizziness” and “ENT”. These explained 32% of the variance.

Conclusions

Empirically identified components classify dizziness into six profiles. This classification takes into account the heterogeneity and multicausality of dizziness and may serve as starting point for research on diagnostic strategies and can be a first step in an evidence based diagnostic approach of dizzy older patients.     

Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

Background

The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms.

Principal Findings

50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods.

Conclusions

Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe “percent bound” value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for short oligonucleotide arrays.     

Household tobacco smoke and admission weight predict severe bronchiolitis in infants independent of deprivation: prospective cohort study

Objectives

To examine demographic, environmental and clinical factors associated with severe bronchiolitis in infants admitted to hospital and quantify the independent effects of these factors.

Design

Prospective cohort study.

Setting

Alder Hey Children''s Hospital, Liverpool, United Kingdom.

Participants

378 infants admitted to hospital with a diagnosis of bronchiolitis, of whom 299 (79%) were antigen positive to respiratory syncytial virus (RSV).

Outcome

Severity of disease during admission, defined as “no need for supplemental oxygen” (reference group), “any need for supplemental oxygen” and “any need for mechanical ventilation”.

Results

Univariate analysis found male sex (p = 0.035) and tobacco smoking by a household member (p<0.001) were associated with need for both supplemental oxygen and mechanical ventilation. Premature birth, low gestation, low birth weight, low admission weight and low corrected age on admission were also associated with need for mechanical ventilation (all p≤0.002). Deprivation scores (IMD 2004) were significantly higher in households where a member smoked compared to non-smoking households (p<0.001). The odds of smoking predicted by deprivation were 7 times higher (95%CI (3.59, 14.03)), when comparing the least and most deprived quintiles of the study population. Family history of atopic disease and deprivation score were not associated with severe disease. Multivariate multinomial logistic regression which initially included all covariates, found household tobacco smoking (adjusted OR = 2.45, 95%CI (1.60, 3.74) predicted need for oxygen supplementation. Household tobacco smoking (adjusted OR = 5.49, (2.78, 10.83)) and weight (kg) on admission (adjusted OR = 0.51, (0.40, 0.65)) were both significant predictors in the final model for mechanical ventilation. The same associations and similar size of effects were found when only children with proven RSV infection were included in analysis.

Conclusions

Low admission weight and householder tobacco smoking increased the risk of severe bronchiolitis in infants admitted to hospital. These effects were independent of a standard deprivation measure. NIHR Study Ref. DHCS/G121/10.     

Receptor activator of NF-kB (RANK) expression in primary tumors associates with bone metastasis occurrence in breast cancer patients

Background

Receptor activator of NFkB (RANK), its ligand (RANKL) and the decoy receptor of RANKL (osteoprotegerin, OPG) play a pivotal role in bone remodeling by regulating osteoclasts formation and activity. RANKL stimulates migration of RANK-expressing tumor cells in vitro, conversely inhibited by OPG.

Materials and Methods

We examined mRNA expression levels of RANKL/RANK/OPG in a publicly available microarray dataset of 295 primary breast cancer patients. We next analyzed RANK expression by immunohistochemistry in an independent series of 93 primary breast cancer specimens and investigated a possible association with clinicopathological parameters, bone recurrence and survival.

Results

Microarray analysis showed that lower RANK and high OPG mRNA levels correlate with longer overall survival (P = 0.0078 and 0.0335, respectively) and disease-free survival (P = 0.059 and 0.0402, respectively). Immunohistochemical analysis of RANK showed a positive correlation with the development of bone metastases (P = 0.023) and a shorter skeletal disease-free survival (SDFS, P = 0.037). Specifically, univariate analysis of survival showed that “RANK-negative” and “RANK-positive” patients had a SDFS of 105.7 months (95% CI: 73.9–124.4) and 58.9 months (95% CI: 34.7–68.5), respectively. RANK protein expression was also associated with accelerated bone metastasis formation in a multivariate analysis (P = 0.029).

Conclusions

This is the first demonstration of the role of RANK expression in primary tumors as a predictive marker of bone metastasis occurrence and SDFS in a large population of breast cancer patients.     

Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models

Background

A number of studies have previously demonstrated that “goodness of fit” is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain.

Results

Here, we propose a novel robustness analysis that aims to determine the “common robustness” of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network.

Conclusions

Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.     

Noradrenergic α₁ receptor antagonist treatment attenuates positive subjective effects of cocaine in humans: a randomized trial

Background

Preclinical research implicates dopaminergic and noradrenergic mechanisms in mediating the reinforcing effects of drugs of abuse, including cocaine. The objective of this study was to evaluate the impact of treatment with the noradrenergic α₁ receptor antagonist doxazosin on the positive subjective effects of cocaine.

Methods

Thirteen non-treatment seeking, cocaine-dependent volunteers completed this single-site, randomized, placebo-controlled, within-subjects study. In one study phase volunteers received placebo and in the other they received doxazosin, with the order counterbalanced across participants. Study medication was masked by over-encapsulating doxazosin tablets and matched placebo lactose served as the control. Study medication treatment was initiated at 1 mg doxazosin or equivalent number of placebo capsules PO/day and increased every three days by 1 mg. After receiving 4 mg doxazosin or equivalent number of placebo capsules participants received masked doses of 20 and 40 mg cocaine IV in that order with placebo saline randomly interspersed to maintain the blind.

Results

Doxazosin treatment was well tolerated and doxazosin alone produced minimal changes in heart rate and blood pressure. During treatment with placebo, cocaine produced dose-dependent increases in subjective effect ratings of “high”, “stimulated”, “like cocaine”, “desire cocaine”, “any drug effect”, and “likely to use cocaine if had access” (p<.001). Doxazosin treatment significantly attenuated the effects of 20 mg cocaine on ratings of “stimulated”, “like cocaine”, and “likely to use cocaine if had access” (p<.05). There were trends for doxazosin to reduce ratings of “stimulated”, “desire cocaine”, and “likely to use cocaine if had access” (p<.10).

Conclusions

Medications that block noradrenergic α₁ receptors, such as doxazosin, may be useful as treatments for cocaine dependence, and should be evaluated further.

Trial Registration

Clinicaltrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01062945","term_id":"NCT01062945"}}NCT01062945     

Systematic validation and atomic force microscopy of non-covalent short oligonucleotide barcode microarrays

Background

Molecular barcode arrays provide a powerful means to analyze cellular phenotypes in parallel through detection of short (20–60 base) unique sequence tags, or “barcodes”, associated with each strain or clone in a collection. However, costs of current methods for microarray construction, whether by in situ oligonucleotide synthesis or ex situ coupling of modified oligonucleotides to the slide surface are often prohibitive to large-scale analyses.

Methodology/Principal Findings

Here we demonstrate that unmodified 20mer oligonucleotide probes printed on conventional surfaces show comparable hybridization signals to covalently linked 5′-amino-modified probes. As a test case, we undertook systematic cell size analysis of the budding yeast Saccharomyces cerevisiae genome-wide deletion collection by size separation of the deletion pool followed by determination of strain abundance in size fractions by barcode arrays. We demonstrate that the properties of a 13K unique feature spotted 20 mer oligonucleotide barcode microarray compare favorably with an analogous covalently-linked oligonucleotide array. Further, cell size profiles obtained with the size selection/barcode array approach recapitulate previous cell size measurements of individual deletion strains. Finally, through atomic force microscopy (AFM), we characterize the mechanism of hybridization to unmodified barcode probes on the slide surface.

Conclusions/Significance

These studies push the lower limit of probe size in genome-scale unmodified oligonucleotide microarray construction and demonstrate a versatile, cost-effective and reliable method for molecular barcode analysis.     

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

Background

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.

Results

Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.

Conclusions

Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users.     

Why Most Biomedical Findings Echoed by Newspapers Turn Out to be False: The Case of Attention Deficit Hyperactivity Disorder

Context

Because positive biomedical observations are more often published than those reporting no effect, initial observations are often refuted or attenuated by subsequent studies.

Objective

To determine whether newspapers preferentially report on initial findings and whether they also report on subsequent studies.

Methods

We focused on attention deficit hyperactivity disorder (ADHD). Using Factiva and PubMed databases, we identified 47 scientific publications on ADHD published in the 1990s and soon echoed by 347 newspapers articles. We selected the ten most echoed publications and collected all their relevant subsequent studies until 2011. We checked whether findings reported in each “top 10” publication were consistent with previous and subsequent observations. We also compared the newspaper coverage of the “top 10” publications to that of their related scientific studies.

Results

Seven of the “top 10” publications were initial studies and the conclusions in six of them were either refuted or strongly attenuated subsequently. The seventh was not confirmed or refuted, but its main conclusion appears unlikely. Among the three “top 10” that were not initial studies, two were confirmed subsequently and the third was attenuated. The newspaper coverage of the “top 10” publications (223 articles) was much larger than that of the 67 related studies (57 articles). Moreover, only one of the latter newspaper articles reported that the corresponding “top 10” finding had been attenuated. The average impact factor of the scientific journals publishing studies echoed by newspapers (17.1 n = 56) was higher (p<0.0001) than that corresponding to related publications that were not echoed (6.4 n = 56).

Conclusion

Because newspapers preferentially echo initial ADHD findings appearing in prominent journals, they report on uncertain findings that are often refuted or attenuated by subsequent studies. If this media reporting bias generalizes to health sciences, it represents a major cause of distortion in health science communication.     

Prevalence of PTSD and Depression among Junior Middle School Students in a Rural Town Far from the Epicenter of the Wenchuan Earthquake in China

Context

On May12^th 2008, a devastating earthquake measuring 8.0 on the Richter scale, struck Wenchuan county and surrounding areas in China. The prevalence of mental illness among children and adolescents in a rural town far from the earthquake epicenter is unknown.

Objective

To assess the prevalence of posttraumatic stress disorder (PTSD) and depression among junior middle school students in a rural town Ningqiang county, 327 km from the earthquake epicenter.

Design, Setting, and Participants

A population-based mental health survey was conducted in March, 2009.

Main Outcome Measure

Survey Self-designed General Condition Survey Scale, Children''s Revised Impact of Event Scale (CRIES-13), and the Depression Self-rating Scale for Children (DSRSC) were used to sample 1,841 junior middle school students in Ningqiang county, ten months after the Wenchuan earthquake.

Results

The prevalence rate of a high-risk for PTSD was 28.4%, with 32.7% among females, 23.8% among males (female vs. male, p<0.001), 38.6% in the severe exposure group and 24.3% in the mild exposure group (severe vs. mild exposure, p<0.001). For depressive symptoms, the overall prevalence was 19.5%, with 24.0% among females, 14.7% among males, 24.5% in the severe exposure group and 17.5% in the mild exposure group (female vs. male, p<0.001; severe vs. mild exposure, p<0.001, respectively). In multivariate analysis, factors such as “having felt despair”, or “danger” and “having own house destroyed or damaged” were significantly associated with PTSD symptoms. Female gender and delayed evacuation in females, and earthquake related experiences in males were significantly associated with depression.

Conclusion

Traumatic events experienced during the earthquake were significantly associated with symptoms of PTSD and depression in children and adolescents, ten months after the Wenchuan earthquake. These data highlight a need for mental health services for children and adolescents in rural areas, far from earthquake epicenters.     

Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer

Background

Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival.

Methodology/Principal Findings

Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation (“batch-effect”). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2^nd validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p<0.01), 1^st validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2^nd validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1^st validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2^nd validation set.

Conclusions/Significance

Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.     

True versus apparent malaria infection prevalence: the contribution of a Bayesian approach

Aims

To present a new approach for estimating the “true prevalence” of malaria and apply it to datasets from Peru, Vietnam, and Cambodia.

Methods

Bayesian models were developed for estimating both the malaria prevalence using different diagnostic tests (microscopy, PCR & ELISA), without the need of a gold standard, and the tests'' characteristics. Several sources of information, i.e. data, expert opinions and other sources of knowledge can be integrated into the model. This approach resulting in an optimal and harmonized estimate of malaria infection prevalence, with no conflict between the different sources of information, was tested on data from Peru, Vietnam and Cambodia.

Results

Malaria sero-prevalence was relatively low in all sites, with ELISA showing the highest estimates. The sensitivity of microscopy and ELISA were statistically lower in Vietnam than in the other sites. Similarly, the specificities of microscopy, ELISA and PCR were significantly lower in Vietnam than in the other sites. In Vietnam and Peru, microscopy was closer to the “true” estimate than the other 2 tests while as expected ELISA, with its lower specificity, usually overestimated the prevalence.

Conclusions

Bayesian methods are useful for analyzing prevalence results when no gold standard diagnostic test is available. Though some results are expected, e.g. PCR more sensitive than microscopy, a standardized and context-independent quantification of the diagnostic tests'' characteristics (sensitivity and specificity) and the underlying malaria prevalence may be useful for comparing different sites. Indeed, the use of a single diagnostic technique could strongly bias the prevalence estimation. This limitation can be circumvented by using a Bayesian framework taking into account the imperfect characteristics of the currently available diagnostic tests. As discussed in the paper, this approach may further support global malaria burden estimation initiatives.     

Trend in obesity prevalence in European adult cohort populations during follow-up since 1996 and their predictions to 2015

Objective

To investigate trends in obesity prevalence in recent years and to predict the obesity prevalence in 2015 in European populations.

Methods

Data of 97 942 participants from seven cohorts involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study participating in the Diogenes project (named as “Diogenes cohort” in the following) with weight measurements at baseline and follow-up were used to predict future obesity prevalence with logistic linear and non-linear (leveling off) regression models. In addition, linear and leveling off models were fitted to the EPIC-Potsdam dataset with five weight measures during the observation period to find out which of these two models might provide the more realistic prediction.

Results

During a mean follow-up period of 6 years, the obesity prevalence in the Diogenes cohort increased from 13% to 17%. The linear prediction model predicted an overall obesity prevalence of about 30% in 2015, whereas the leveling off model predicted a prevalence of about 20%. In the EPIC-Potsdam cohort, the shape of obesity trend favors a leveling off model among men (R² = 0.98), and a linear model among women (R² = 0.99).

Conclusion

Our data show an increase in obesity prevalence since the 1990ies, and predictions by 2015 suggests a sizeable further increase in European populations. However, the estimates from the leveling off model were considerably lower.     

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

Objectives

To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.

Methods

We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.

Results

Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.

Conclusion

Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications.     

Comparing Platforms for C. elegans Mutant Identification Using High-Throughput Whole-Genome Sequencing

Background

Whole-genome sequencing represents a promising approach to pinpoint chemically induced mutations in genetic model organisms, thereby short-cutting time-consuming genetic mapping efforts.

Principal Findings

We compare here the ability of two leading high-throughput platforms for paired-end deep sequencing, SOLiD (ABI) and Genome Analyzer (Illumina; “Solexa”), to achieve the goal of mutant detection. As a test case we used a mutant C. elegans strain that harbors a mutation in the lsy-12 locus which we compare to the reference wild-type genome sequence. We analyzed the accuracy, sensitivity, and depth-coverage characteristics of the two platforms. Both platforms were able to identify the mutation that causes the phenotype of the mutant C. elegans strain, lsy-12. Based on a 4 MB genomic region in which individual variants were validated by Sanger sequencing, we observe tradeoffs between rates of false positives and false negatives when using both platforms under similar coverage and mapping criteria.

Significance

In conclusion, whole-genome sequencing conducted by either platform is a viable approach for the identification of single-nucleotide variations in the C. elegans genome.     

Efficient silencing of gene expression by an ASON-bulge-DNAzyme complex

Background

DNAzymes are DNA molecules that can directly cleave cognate mRNA, and have been developed to silence gene expression for research and clinical purposes. The advantage of DNAzymes over ribozymes is that they are inexpensive to produce and exhibit good stability. The “10-23 DNA enzyme” is composed of a catalytic domain of 15 deoxynucleotides, flanked by two substrate-recognition domains of approximately eight nucleotides in each direction, which provides the complementary sequence required for specific binding to RNA substrates. However, these eight nucleotides might not afford sufficient binding energy to hold the RNA substrate along with the DNAzyme, which would interfere with the efficiency of the DNAzyme or cause side effects, such as the cleavage of non-cognate mRNAs.

Methodology

In this study, we inserted a nonpairing bulge at the 5′ end of the “10–23 DNA enzyme” to enhance its efficiency and specificity. Different sizes of bulges were inserted at different positions in the 5′ end of the DNAzyme. The non-matching bulge will avoid strong binding between the DNAzyme and target mRNA, which may interfere with the efficiency of the DNAzyme.

Conclusions

Our novel DNAzyme constructs could efficiently silence the expression of target genes, proving a powerful tool for gene silencing. The results showed that the six oligo bulge was the most effective when the six oligo bulge was 12–15 bp away from the core catalytic domain.     

Stochastic resonance reveals "pilot light" expression in mammalian genes

Background

Microarrays are widely used for estimation of expression of thousands of genes in a biological sample. The resolution ability of this method is limited by the background noise. Low expressed genes are detected with insufficient reliability and expression of many genes is never detected at all.

Methodology/Principal Findings

We have applied the principles of stochastic resonance to detect expression of genes from microarray signals below the background noise level. We report the periodic pattern detected in genes called “Absent” by traditional analysis. The pattern is consistent with expression of the conventionally detected genes and specific to the tissue of origin. This effect is corroborated by the analysis of oscillating gene expression in mouse (M.musculus) and yeast (S. cerevisae).

Conclusion/Significance

Most genes usually considered silent are in fact expressed at a very low level. Stochastic resonance can be applied to detect changes in expression pattern of low-expressed genes as well as for the validation of the probe performance in microarrays.