Development,Calibration, and Validation of a U.S. White Male Population-Based Simulation Model of Esophageal Adenocarcinoma

Background

The incidence of esophageal adenocarcinoma (EAC) has risen rapidly in the U.S. and western world. The aim of the study was to begin the investigation of this rapid rise by developing, calibrating, and validating a mathematical disease simulation model of EAC using available epidemiologic data.

Methods

The model represents the natural history of EAC, including the essential biologic health states from normal mucosa to detected cancer. Progression rates between health states were estimated via calibration, which identified distinct parameter sets producing model outputs that fit epidemiologic data; specifically, the prevalence of pre-cancerous lesions and EAC cancer incidence from the published literature and Surveillance, Epidemiology, and End Results (SEER) data. As an illustrative example of a clinical and policy application, the calibrated and validated model retrospectively analyzed the potential benefit of an aspirin chemoprevention program.

Results

Model outcomes approximated calibration targets; results of the model''s fit and validation are presented. Approximately 7,000 cases of EAC could have been prevented over a 30-year period if all white males started aspirin chemoprevention at age 40 in 1965.

Conclusions

The model serves as the foundation for future analyses to determine a cost-effective screening and management strategy to prevent EAC morbidity and mortality.     

Bifactor Item Response Theory Model of Acute Stress Response

Background

Better understanding of acute stress responses is important for revision of DSM-5. However, the latent structure and relationship between different aspects of acute stress responses haven’t been clarified comprehensively. Bifactor item response model may help resolve this problem.

Objective

The purpose of this study is to develop a statistical model of acute stress responses, based on data from earthquake rescuers using Acute Stress Response Scale (ASRS). Through this model, we could better understand acute stress responses comprehensively, and provide preliminary information for computerized adaptive testing of stress responses.

Methods

Acute stress responses of earthquake rescuers were evaluated using ASRS, and state/trait anxiety were assessed using State-trait Anxiety Inventory (STAI). A hierarchical item response model (bifactor model) was used to analyze the data. Additionally, we tested this hierarchical model with model fit comparisons with one-dimensional and five-dimensional models. The correlations among acute stress responses and state/trait anxiety were compared, based on both the five-dimensional and bifactor models.

Results

Model fit comparisons showed bifactor model fit the data best. Item loadings on general and specific factors varied greatly between different aspects of stress responses. Many symptoms (40%) of physiological responses had positive loadings on general factor, and negative loadings on specific factor of physiological responses, while other stress responses had positive loadings on both general and specific factors. After extracting general factor of stress responses using bifactor analysis, significant positive correlations between physiological responses and state/trait anxiety (r = 0.185/0.112, p<0.01) changed into negative ones (r = −0.177/−0.38, p<0.01).

Conclusion

Our results demonstrated bifactor structure of acute stress responses, and positive and negative correlations between physiological responses and stress responses suggested physiological responses could have negative feedback on severity of stress responses. This finding has not been convincingly demonstrated in previous research.     

Concordance of Gene Expression and Functional Correlation Patterns across the NCI-60 Cell Lines and the Cancer Genome Atlas Glioblastoma Samples

Background

The NCI-60 is a panel of 60 diverse human cancer cell lines used by the U.S. National Cancer Institute to screen compounds for anticancer activity. We recently clustered genes based on correlation of expression profiles across the NCI-60. Many of the resulting clusters were characterized by cancer-associated biological functions. The set of curated glioblastoma (GBM) gene expression data from the Cancer Genome Atlas (TCGA) initiative has recently become available. Thus, we are now able to determine which of the processes are robustly shared by both the immortalized cell lines and clinical cancers.

Results

Our central observation is that some sets of highly correlated genes in the NCI-60 expression data are also highly correlated in the GBM expression data. Furthermore, a “double fishing” strategy identified many sets of genes that show Pearson correlation ≥0.60 in both the NCI-60 and the GBM data sets relative to a given “bait” gene. The number of such gene sets far exceeds the number expected by chance.

Conclusion

Many of the gene-gene correlations found in the NCI-60 do not reflect just the conditions of cell lines in culture; rather, they reflect processes and gene networks that also function in vivo. A number of gene network correlations co-occur in the NCI-60 and GBM data sets, but there are others that occur only in NCI-60 or only in GBM. In sum, this analysis provides an additional perspective on both the utility and the limitations of the NCI-60 in furthering our understanding of cancers in vivo.     

Evaluation of Construct and Criterion Validity for the ‘Liverpool Osteoarthritis in Dogs’ (LOAD) Clinical Metrology Instrument and Comparison to Two Other Instruments

Objective

To test the ‘Liverpool Osteoarthritis in Dogs’ (LOAD) questionnaire for construct and criterion validity, and to similarly test the Helsinki Chronic Pain Index (HCPI) and the Canine Brief Pain Inventory (CBPI).

Design

Prospective Study.

Animals

222 dogs with osteoarthritis.

Procedure

Osteoarthritis was diagnosed in a cohort of dogs on the basis of clinical history and orthopedic examination. Force-platform analysis was performed and a “symmetry index” for peak vertical force (PVF) was calculated. Owners completed LOAD, CBPI and HCPI instruments. As a test of construct validity, inter-instrument correlations were calculated. As a test of criterion validity, the correlations between instrument scores and PVF symmetry scores were calculated. Additionally, internal consistency of all instruments was calculated and compared to those previously reported. Factor analysis is reported for the first time for LOAD, and is compared to that previously reported for CBPI and HCPI.

Results

Significant moderate correlations were found between all instruments, implying construct validity for all instruments. Significant weak correlations were found between LOAD scores and PVF symmetry index, and between CBPI scores and PVF symmetry index.

Conclusion and Clinical Relevance

LOAD is an owner-completed clinical metrology instrument that can be recommended for the measurement of canine osteoarthritis. It is convenient to use, validated and, as demonstrated here for the first time, has a correlation with force-platform data.     

Paracoccidioides lutzii Plp43 Is an Active Glucanase with Partial Antigenic Identity with P. brasiliensis gp43

Background

Paracoccidioides brasiliensis and P. lutzii cause paracoccidioidomycosis (PCM). P. brasiliensis main diagnostic antigen is glycoprotein gp43, and its peptide sequence is 81% identical with a P. lutzii ortholog here called Plp43. P. lutzii (“Pb01-like”) apparently predominates in Midwestern/Northern Brazil, where high percentages of false-negative reactions using P. brasiliensis antigens have recently been reported. The aim of this work was to produce recombinant Plp43 to study its antigenic identity with gp43.

Methodology

We expressed rPlp43 as a secreted major component in Pichia pastoris and studied its reactivity in immunoblot with PCM patients'' sera from Southwestern and Midwestern Brazil.

Principal Findings

We showed that rPlp43 is not glycosylated and bears glucanase activity. The protein did not react with anti-gp43 monoclonal antibodies in immunoblot, suggesting absence of the corresponding gp43 epitopes. Nevertheless, common epitope(s) might exist, considering that gp43-positive PCM sera recognized rPlp43 in immunoblot, while gp43-negative sera (33 out of 51) from patients resident in Midwestern Brazil were also rPlp43-negative. Two genotyped P. lutzii were from patients with gp43-negative sera, suggesting that non-reactive sera are from patients infected with this species.

Conclusion

Our data suggest that gp43 and Plp43 bear one or only a few common epitopes and that gp43 cannot be used in diagnosis of PCM patients infected with P. lutzii probably because Plp43 is poorly expressed during infection.     

Real-Time 3-Dimensional Echocardiographic Assessment of Ventricular Volume,Mass, and Function in Human Fetuses

Objectives

We sought to determine the feasibility and reproducibility of real-time 3-dimensional echocardiography (RT3DE) for evaluation of cardiac volume, mass, and function and to characterize maturational changes of these measurements in human fetuses.

Methods

Eighty pregnant women in the 2^nd and 3^rd trimesters (59 with normal fetuses and 21 with fetuses with congenital heart disease [CHD]) were enrolled. We acquired RT3DE images using a matrix-array transducer. RT3DE measurements of volume, mass, stroke volume (SV), combined cardiac output (CCO), and ejection fraction (EF) were obtained. Images were scored and analyzed by two blinded independent observers. Inter- and intraobserver variabilities and correlations between fetal cardiac indices and gestational age were determined.

Results

Fifty-two of 59 normal data sets (88%) and 9 of 21 CHD data sets (43%) were feasible for analysis. In normal fetuses, the right ventricle (RV) is larger than the left ventricle (LV) (P<0.05), but no difference exists between the LV and RV in mass, SV, CO, and CO/CCO. The EFs for the LV and RV were diminished; the RVSV/LVSV was reduced in CHD fetuses compared with normal fetuses (P<0.05). Fetal ventricular volumes, mass, SV, and CCO fit best into exponential curves with gestational age, but LVEF, RVEF, and RVSV/LVSV remain relatively constant.

Conclusions

RT3DE is feasible and reproducible for assessment of LV and RV volume, mass, and function, especially in normal fetuses. Gestational growth of these measures, except for EF, is exponential in normal and CHD fetuses. CHD fetuses exhibit diminished LV and RV EFs.     

The Use of Bivariate Spatial Modeling of Questionnaire and Parasitology Data to Predict the Distribution of Schistosoma haematobium in Coastal Kenya

Background

Questionnaires of reported blood in urine (BIU) distributed through the existing school system provide a rapid and reliable method to classify schools according to the prevalence of Schistosoma haematobium, thereby helping in the targeting of schistosomiasis control. However, not all schools return questionnaires and it is unclear whether treatment is warranted in such schools. This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S. haematobium at every school along the Kenyan coast.

Methodology

Data from a questionnaire survey conducted by the Kenya Ministry of Education in Coast Province in 2009 were combined with available parasitological and environmental data in a Bayesian bivariate spatial model. This modeled the relationship between BIU data and environmental covariates, as well as the relationship between BIU and S. haematobium infection prevalence, to predict S. haematobium infection prevalence at all schools in the study region. Validation procedures were implemented to assess the predictive accuracy of endemicity classification.

Principal Findings

The prevalence of BIU was negatively correlated with distance to nearest river and there was considerable residual spatial correlation at small (∼15 km) spatial scales. There was a predictable relationship between the prevalence of reported BIU and S. haematobium infection. The final model exhibited excellent sensitivity (0.94) but moderate specificity (0.69) in identifying low (<10%) prevalence schools, and had poor performance in differentiating between moderate and high prevalence schools (sensitivity 0.5, specificity 1).

Conclusions

Schistosomiasis is highly focal and there is a need to target treatment on a school-by-school basis. The use of bivariate spatial modelling can supplement questionnaire data to identify schools requiring mass treatment, but is unable to distinguish between moderate and high prevalence schools.     

Gene set enrichment analysis for multiple continuous phenotypes

Background

Gene set analysis (GSA) methods test the association of sets of genes with phenotypes in gene expression microarray studies. While GSA methods on a single binary or categorical phenotype abounds, little attention has been paid to the case of a continuous phenotype, and there is no method to accommodate correlated multiple continuous phenotypes.

Result

We propose here an extension of the linear combination test (LCT) to its new version for multiple continuous phenotypes, incorporating correlations among gene expressions of functionally related gene sets, as well as correlations among multiple phenotypes. Further, we extend our new method to its nonlinear version, referred as nonlinear combination test (NLCT), to test potential nonlinear association of gene sets with multiple phenotypes. Simulation study and a real microarray example demonstrate the practical aspects of the proposed methods.

Conclusion

The proposed approaches are effective in controlling type I errors and powerful in testing associations between gene-sets and multiple continuous phenotypes. They are both computationally effective. Naively (univariately) analyzing a group of multiple correlated phenotypes could be dangerous. R-codes to perform LCT and NLCT for multiple continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-260) contains supplementary material, which is available to authorized users.     

Using Highly Detailed Administrative Data to Predict Pneumonia Mortality

Background

Mortality prediction models generally require clinical data or are derived from information coded at discharge, limiting adjustment for presenting severity of illness in observational studies using administrative data.

Objectives

To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days.

Research Design

After dividing the dataset into derivation and validation sets, we created a hierarchical generalized linear mortality model that included patient demographics, comorbidities, medications, therapies, and diagnostic tests administered in the first 2 hospital days. We then applied the model to the validation set.

Subjects

Patients aged ≥18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.’s Perspective database.

Measures

In hospital mortality.

Results

The derivation cohort included 200,870 patients and the validation cohort had 50,037. Mortality was 7.2%. In the multivariable model, 3 demographic factors, 25 comorbidities, 41 medications, 7 diagnostic tests, and 9 treatments were associated with mortality. Factors that were most strongly associated with mortality included receipt of vasopressors, non-invasive ventilation, and bicarbonate. The model had a c-statistic of 0.85 in both cohorts. In the validation cohort, deciles of predicted risk ranged from 0.3% to 34.3% with observed risk over the same deciles from 0.1% to 33.7%.

Conclusions

A mortality model based on detailed administrative data available in the first 2 hospital days had good discrimination and calibration. The model compares favorably to clinically based prediction models and may be useful in observational studies when clinical data are not available.     

Positive and Negative Relationship between Anxiety and Depression of Patients in Pain: A Bifactor Model Analysis

Background

The relationship between anxiety and depression in pain patients has not been clarified comprehensively. Previous research has identified a common factor in anxiety and depression, which may explain why depression and anxiety are strongly correlated. However, the specific clinical features of anxiety and depression seem to pull in opposite directions.

Objective

The purpose of this study is to develop a statistical model of depression and anxiety, based on data from pain patients using Hospital Anxiety and Depression Scale (HADS). This model should account for the positive correlation between depression and anxiety in terms of a general factor and also demonstrate a latent negative correlation between the specific factors underlying depression and anxiety.

Methods

The anxiety and depression symptoms of pain patients were evaluated using the HADS and the severity of their pain was assessed with the visual analogue scale (VAS). We developed a hierarchical model of the data using an IRT method called bifactor analysis. In addition, we tested this hierarchical model with model fit comparisons with unidimensional, bidimensional, and tridimensional models. The correlations among anxiety, depression, and pain severity were compared, based on both the bidimensional model and our hierarchical model.

Results

The bidimensional model analysis found that there was a large positive correlation between anxiety and depression (r = 0.638), and both scores were significantly positively correlated with pain severity. After extracting general factor of distress using bifactor analysis, the specific factors underlying anxiety and depression were weakly but significantly negatively correlated (r = −0.245) and only the general factor was significantly correlated with pain severity. Compared with the three first-order models, the bifactor hierarchical model had the best model fit.

Conclusion

Our results support the hypothesis that apart from distress, anxiety and depression are inversely correlated. This finding has not been convincingly demonstrated in previous research.     

Different anthropometric adiposity measures and their association with cardiovascular disease risk factors: a meta-analysis

Objectives

To investigate which anthropometric adiposity measure has the strongest association with cardiovascular disease (CVD) risk factors in Caucasian men and women without a history of CVD.

Design

Systematic review and meta-analysis.

Methods

We searched databases for studies reporting correlations between anthropometric adiposity measures and CVD risk factors in Caucasian subjects without a history of CVD. Body mass index (BMI), waist circumference, waist-to-hip ratio, waist-to-height ratio and body fat percentage were considered the anthropometric adiposity measures. Primary CVD risk factors were: systolic blood pressure, diastolic blood pressure, high density lipoprotein (HDL) cholesterol, triglycerides and fasting glucose. Two independent reviewers performed abstract, full text and data selection.

Results

Twenty articles were included describing 21,618 males and 24,139 females. Waist circumference had the strongest correlation with all CVD risk factors for both men and women, except for HDL and LDL in men. When comparing BMI with waist circumference, the latter showed significantly better correlations to CVD risk factors, except for diastolic blood pressure in women and HDL and total cholesterol in men.

Conclusions

We recommend the use of waist circumference in clinical and research studies above other anthropometric adiposity measures, especially compared with BMI, when evaluating CVD risk factors.     

International genomic evaluation methods for dairy cattle

Background

Genomic evaluations are rapidly replacing traditional evaluation systems used for dairy cattle selection. Higher reliabilities from larger genotype files promote cooperation across country borders. Genomic information can be exchanged across countries using simple conversion equations, by modifying multi-trait across-country evaluation (MACE) to account for correlated residuals originating from the use of foreign evaluations, or by multi-trait analysis of genotypes for countries that use the same reference animals.

Methods

Traditional MACE assumes independent residuals because each daughter is measured in only one country. Genomic MACE could account for residual correlations using daughter equivalents from genomic data as a fraction of the total in each country and proportions of bulls shared. MACE methods developed to combine separate within-country genomic evaluations were compared to direct, multi-country analysis of combined genotypes using simulated genomic and phenotypic data for 8,193 bulls in nine countries.

Results

Reliabilities for young bulls were much higher for across-country than within-country genomic evaluations as measured by squared correlations of estimated with true breeding values. Gains in reliability from genomic MACE were similar to those of multi-trait evaluation of genotypes but required less computation. Sharing of reference genotypes among countries created large residual correlations, especially for young bulls, that are accounted for in genomic MACE.

Conclusions

International genomic evaluations can be computed either by modifying MACE to account for residual correlations across countries or by multi-trait evaluation of combined genotype files. The gains in reliability justify the increased computation but require more cooperation than in previous breeding programs.     

Prognostic role of c-met expression in breast cancer patients

Background

Hepatocyte growth factor plays an important role in tumor growth, metastasis and angiogenesis. C-met is HGF''s high affinity receptor.

Aim

The aim of the study was to assess the correlations between c-met expression and clinic-pathological factors in breast cancer tissues. Furthermore, the purpose of the study was to evaluate the prognostic value of the hepatocyte growth factor receptor (HGFR, c-met) expressions in homogenous group of breast cancer patients.

Materials and methods

Tumor samples were collected from 302 patients with breast carcinoma treated with primary surgery. We have assessed the percentage of tumor cells with c-met expression, the intensity of reaction and the ratio of these two factors—immunoreactivity according to the Remmele score.

Results

We have observed no correlations between HGFR immunoreactivities and clinical parameters (tumor size, grade, axillary lymph node status, age). In 5-year observation we have found prognostic value of assessing c-met immunoreactivity in primary tumor.

Conclusion

Our study has revealed prognostic value of c-met. Unlike in other authors’ studies, our patients’ group is very homogenous which might contribute to obtained results.     

A Genetic Deconstruction of Neurocognitive Traits in Schizophrenia and Bipolar Disorder

Background

Impairments in cognitive functions are common in patients suffering from psychiatric disorders, such as schizophrenia and bipolar disorder. Cognitive traits have been proposed as useful for understanding the biological and genetic mechanisms implicated in cognitive function in healthy individuals and in the dysfunction observed in psychiatric disorders.

Methods

Sets of genes associated with a range of cognitive functions often impaired in schizophrenia and bipolar disorder were generated from a genome-wide association study (GWAS) on a sample comprising 670 healthy Norwegian adults who were phenotyped for a broad battery of cognitive tests. These gene sets were then tested for enrichment of association in GWASs of schizophrenia and bipolar disorder. The GWAS data was derived from three independent single-centre schizophrenia samples, three independent single-centre bipolar disorder samples, and the multi-centre schizophrenia and bipolar disorder samples from the Psychiatric Genomics Consortium.

Results

The strongest enrichments were observed for visuospatial attention and verbal abilities sets in bipolar disorder. Delayed verbal memory was also enriched in one sample of bipolar disorder. For schizophrenia, the strongest evidence of enrichment was observed for the sets of genes associated with performance in a colour-word interference test and for sets associated with memory learning slope.

Conclusions

Our results are consistent with the increasing evidence that cognitive functions share genetic factors with schizophrenia and bipolar disorder. Our data provides evidence that genetic studies using polygenic and pleiotropic models can be used to link specific cognitive functions with psychiatric disorders.     

DupChecker: a bioconductor package for checking high-throughput genomic data redundancy in meta-analysis

Background

Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates could lead false positive finding, misleading clustering pattern or model over-fitting issue, etc in the subsequent data analysis.

Results

We developed a Bioconductor package Dupchecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. A real data example was demonstrated to show the usage and output of the package.

Conclusions

Researchers may not pay enough attention to checking and removing duplicated samples, and then data contamination could make the results or conclusions from meta-analysis questionable. We suggest applying DupChecker to examine all gene expression data sets before any data analysis step.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-323) contains supplementary material, which is available to authorized users.     

Ineffectiveness of Reverse Wording of Questionnaire Items: Let’s Learn from Cows in the Rain

Objective

We examined the effectiveness of reverse worded items as a means of reducing or preventing response bias. We first distinguished between several types of response bias that are often confused in literature. We next developed arguments why reversing items is probably never a good way to address response bias. We proposed testing whether reverse wording affects response bias with item-level data from the Multidimensional Fatigue Inventory (MFI-20), an instrument that contains reversed worded items.

Methods

With data from 700 respondents, we compared scores on items that were similar with respect either to content or to direction of wording. Psychometric properties of sets of these items worded in the same direction were compared with sets consisting of both straightforward and reversed worded items.

Results

We did not find evidence that ten reverse-worded items prevented response bias. Instead, the data suggest scores were contaminated by respondent inattention and confusion.

Conclusions

Using twenty items, balanced for scoring direction, to assess fatigue did not prevent respondents from inattentive or acquiescent answering. Rather, fewer mistakes are made with a 10-item instrument with items posed in the same direction. Such a format is preferable for both epidemiological and clinical studies.     

All-Arthroscopic versus Mini-Open Repair of Small to Large Sized Rotator Cuff Tears: A Meta-Analysis of Clinical Outcomes

Purpose

The purpose of this study was to compare clinical outcomes of patients with full-thickness small to large sized tears undergoing all-arthroscopic versus mini-open rotator cuff repair.

Method

A literature search for electronic databases and references for eligible studies was conducted through Medline, Embase and Cochrane library between 1969 and 2013.

Results

A total of 12 comparative studies (n = 770 patients) were included. Pooled results showed: there were no differences in function outcome, pain scores, retear rate or the incidence of adhesive capsulitis between all arthroscopic and mini-open repair groups.

Conclusions

There were no differences in outcomes between the arthroscopic and mini-open rotator cuff repair techniques, they should be considered alternative treatment options.

Level of Evidence

Level IV, Meta analysis.     

Identifying ontogenetic, environmental and individual components of forest tree growth

Background and Aims

This study aimed to identify and characterize the ontogenetic, environmental and individual components of forest tree growth. In the proposed approach, the tree growth data typically correspond to the retrospective measurement of annual shoot characteristics (e.g. length) along the trunk.

Methods

Dedicated statistical models (semi-Markov switching linear mixed models) were applied to data sets of Corsican pine and sessile oak. In the semi-Markov switching linear mixed models estimated from these data sets, the underlying semi-Markov chain represents both the succession of growth phases and their lengths, while the linear mixed models represent both the influence of climatic factors and the inter-individual heterogeneity within each growth phase.

Key Results

On the basis of these integrative statistical models, it is shown that growth phases are not only defined by average growth level but also by growth fluctuation amplitudes in response to climatic factors and inter-individual heterogeneity and that the individual tree status within the population may change between phases. Species plasticity affected the response to climatic factors while tree origin, sampling strategy and silvicultural interventions impacted inter-individual heterogeneity.

Conclusions

The transposition of the proposed integrative statistical modelling approach to cambial growth in relation to climatic factors and the study of the relationship between apical growth and cambial growth constitute the next steps in this research.