Clinical and molecular characterization of the potential CF disease modifier syntaxin 1A

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator gene (CFTR). Disease severity in CF varies greatly, and sibling studies strongly indicate that genes other than CFTR modify disease outcome. Syntaxin 1A (STX1A) has been reported as a negative regulator of CFTR and other ion channels. We hypothesized that STX1A variants act as a CF modifier by influencing the remaining function of mutated CFTR. We identified STX1A variants by genomic resequencing patients from the Bernese CF Patient Data Registry and applied linear mixed model analysis to establish genotype-phenotype correlations, revealing STX1A rs4363087 (c.467-38A>G) to significantly influence lung function. The same STX1A risk allele was recognized in the European CF Twin and Sibling Study (P=0.0027), demonstrating that the genotype-phenotype association of STX1A to CF disease severity is robust enough to allow replication in two independent CF populations. rs4363087 is in linkage disequilibrium to the exonic variant rs2228607 (c.204C>T). Considering that neither rs4363087 nor rs2228607 changes the amino-acid sequence of STX1A, we investigated their effects on mRNA level. We show that rs2228607 reinforces aberrant splicing of STX1A mRNA, leading to nonsense-mediated mRNA decay. In conclusion, we demonstrate the clinical relevance of STX1A variants in CF, and evidence the functional relevance of STX1A variant rs2228607 at molecular level. Our findings show that genes interacting with CFTR can modify CF disease progression.European Journal of Human Genetics advance online publication, 10 April 2013; doi:10.1038/ejhg.2013.57.

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

MODELING DIFFERENTIATED TREATMENT EFFECTS FOR MULTIPLE OUTCOMES DATA

Multiple outcomes data are commonly used to characterize treatment effects in medical research, for instance, multiple symptoms to characterize potential remission of a psychiatric disorder. Often either a global, i.e. symptom-invariant, treatment effect is evaluated. Such a treatment effect may over generalize the effect across the outcomes. On the other hand individual treatment effects, varying across all outcomes, are complicated to interpret, and their estimation may lose precision relative to a global summary. An effective compromise to summarize the treatment effect may be through patterns of the treatment effects, i.e. "differentiated effects." In this paper we propose a two-category model to differentiate treatment effects into two groups. A model fitting algorithm and simulation study are presented, and several methods are developed to analyze heterogeneity presenting in the treatment effects. The method is illustrated using an analysis of schizophrenia symptom data.

ON MARGINALIZED MULTILEVEL MODELS AND THEIR COMPUTATION

Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster-dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate this approach on a cerebrovascular deficiency crossover trial.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

Long-term gas exchange characteristics as marker of deterioration in patients with cystic fibrosis

Background and Aim In patients with cystic fibrosis (CF) the architecture of the developing lungs and the ventilation of lung units are progressively affected, influencing intrapulmonary gas mixing and gas exchange. We examined the long-term course of blood gas measurements in relation to characteristics of lung function and the influence of different CFTR genotype upon this process. Methods Serial annual measurements of PaO2 and PaCO2 assessed in relation to lung function, providing functional residual capacity (FRCpleth), lung clearance index (LCI), trapped gas (VTG), airway resistance (sReff), and forced expiratory indices (FEV1, FEF50), were collected in 178 children (88 males; 90 females) with CF, over an age range of 5 to 18 years. Linear mixed model analysis and binary logistic regression analysis were used to define predominant lung function parameters influencing oxygenation and carbon dioxide elimination. Results PaO2 decreased linearly from age 5 to 18 years, and was mainly associated with FRCpleth, (p < 0.0001), FEV1 (p < 0.001), FEF50 (p < 0.002), and LCI (p < 0.002), indicating that oxygenation was associated with the degree of pulmonary hyperinflation, ventilation inhomogeneities and impeded airway function. PaCO2 showed a transitory phase of low PaCO2 values, mainly during the age range of 5 to 12 years. Both PaO2 and PaCO2 presented with different progression slopes within specific CFTR genotypes. Conclusion In the long-term evaluation of gas exchange characteristics, an association with different lung function patterns was found and was closely related to specific genotypes. Early examination of blood gases may reveal hypocarbia, presumably reflecting compensatory mechanisms to improve oxygenation.

Knowledge and valuation of Andean agroforestry species: the role of sex, age, and migration among members of a rural community in Bolivia

Background Agroforestry is a sustainable land use method with a long tradition in the Bolivian Andes. A better understanding of people’s knowledge and valuation of woody species can help to adjust actor-oriented agroforestry systems. In this case study, carried out in a peasant community of the Bolivian Andes, we aimed at calculating the cultural importance of selected agroforestry species, and at analysing the intracultural variation in the cultural importance and knowledge of plants according to peasants’ sex, age, and migration. Methods Data collection was based on semi-structured interviews and freelisting exercises. Two ethnobotanical indices (Composite Salience, Cultural Importance) were used for calculating the cultural importance of plants. Intracultural variation in the cultural importance and knowledge of plants was detected by using linear and generalised linear (mixed) models. Results and discussion The culturally most important woody species were mainly trees and exotic species (e.g. Schinus molle, Prosopis laevigata, Eucalyptus globulus). We found that knowledge and valuation of plants increased with age but that they were lower for migrants; sex, by contrast, played a minor role. The age effects possibly result from decreasing ecological apparency of valuable native species, and their substitution by exotic marketable trees, loss of traditional plant uses or the use of other materials (e.g. plastic) instead of wood. Decreasing dedication to traditional farming may have led to successive abandonment of traditional tool uses, and the overall transformation of woody plant use is possibly related to diminishing medicinal knowledge. Conclusions Age and migration affect how people value woody species and what they know about their uses. For this reason, we recommend paying particular attention to the potential of native species, which could open promising perspectives especially for the young migrating peasant generation and draw their interest in agroforestry. These native species should be ecologically sound and selected on their potential to provide subsistence and promising commercial uses. In addition to offering socio-economic and environmental services, agroforestry initiatives using native trees and shrubs can play a crucial role in recovering elements of the lost ancient landscape that still forms part of local people’s collective identity.

Factors that trigger emergency physicians to contact a poison centre: findings from a Swiss study.

OBJECTIVE Poison centres offer rapid and comprehensive support for emergency physicians managing poisoned patients. This study investigates institutional, case-specific and poisoning-specific factors which influence the decision of emergency physicians to contact a poison centre. METHODS Retrospective, consecutive review of all poisoning-related admissions to the emergency departments (EDs) of a primary care hospital and a university hospital-based tertiary referral centre during 2007. Corresponding poison centre consultations were extracted from the poison centre database. Data were matched and analysed by logistic regression and generalised linear mixed models. RESULTS 545 poisonings were treated in the participating EDs (350 (64.2%) in the tertiary care centre, 195 (35.8%) in the primary care hospital). The poison centre was consulted in 62 (11.4%) cases (38 (61.3%) by the tertiary care centre and 24 (38.7%) by the primary care hospital). Factors significantly associated with poison centre consultation included gender (female vs male) (OR 2.99; 95% CI 1.69 to 5.29; p<0.001), number of ingested substances (>1 vs 1) (OR 2.84; 95% CI 1.65 to 4.9; p<0.001) and situation (accidental vs intentional) (OR 2.76; 95% CI 1.05 to 7.25; p=0.039). In contrast, age, medical history and hospital size did not influence poison centre consultation. Poison centre consultation was significantly higher during the week, and significantly less during night shifts. The poison centre was consulted significantly more when patients were admitted to intensive care units (OR 5.81; 95% CI 3.25 to 10.37; p<0.001). Asymptomatic and severe versus mild cases were associated with more frequent consultation (OR 4.48; 95% CI 1.78 to 11.26; p=0.001 and OR 2.76; 95% CI 1.42 to 5.38; p=0.003). CONCLUSIONS We found low rates of poison centre consultation by emergency physicians. It appears that intensive care unit admission and other factors reflecting either complexity or uncertainty of the clinical situation are the strongest predictors for poison centre consultation. Hospital size did not influence referral behaviour.

Only Neochordoplasty Achieves Physiological Trans-valvular Pressure Gradients After Repair Of Acute Posterior Leaflet Prolapse In Porcine Mitral Valves

Objective: Minimizing resection and preserving leaflet tissue has been previously shown to be beneficial for mitral valve function and leaflet kinematics after repair of acute posterior leaflet prolapse in porcine valves. We examined the effects of different additional methods of mitral valve repair (neochordoplasty, ring annuloplasty, edge-to-edge repair and triangular resection) on hemodynamics at different heart rates in an experimental model. Methods: Severe acute P2 prolapse was created in eight porcine mitral valves by resecting the posterior marginal chordae. Valve hemodynamics was quantified under pulsatile conditions in an in vitro heart simulator before and after surgical manipulation. Mitral regurgitation was corrected using four different methods of repair on the same valve: neochordoplasty with expanded polytetrafluoroethylene sutures alone and together with ring annuloplasty, edge-to-edge repair and triangular resection, both with non-restrictive annuloplasty. Residual mitral valve leak, trans-valvular pressure gradients, flow and cardiac output were measured at 60 and 80 beats/min. A validated statistical linear mixed model was used to analyze the effect of treatment. The p values were calculated using a two-sided Wald test. Results: Only neochordoplasty with expanded polytetrafluoroethylene sutures but without ring annuloplasty achieved similar hemodynamics compared to those of the native mitral valve (p range 0.071-0.901). Trans-valvular diastolic pressure gradients were within a physiologic range but significantly higher than those of the native valve following neochordoplasty with ring annuloplasty (p=0.000), triangular resection (p=0.000) and edge-to-edge repair (p=0.000). Neochordoplasty alone was significantly better in terms of hemodynamic than neochordoplasty with a ring annuloplasty (p=0.000). These values were stable regardless of heart rate or ring size. Conclusions: Neochordoplasty without ring annuloplasty is the only repair technique able to achieve almost native physiological hemodynamics after correction of leaflet prolapse in a porcine experimental model of acute chordal rupture.

Effect of an intervertebral disk spacer on stiffness after monocortical screw/polymethylmethacrylate fixation in simulated and cadaveric canine cervical vertebral columns.

OBJECTIVE To determine the biomechanical effect of an intervertebral spacer on construct stiffness in a PVC model and cadaveric canine cervical vertebral columns stabilized with monocortical screws/polymethylmethacrylate (PMMA). STUDY DESIGN Biomechanical study. SAMPLE POPULATION PVC pipe; cadaveric canine vertebral columns. METHODS PVC model-PVC pipe was used to create a gap model mimicking vertebral endplate orientation and disk space width of large-breed canine cervical vertebrae; 6 models had a 4-mm gap with no spacer (PVC group 1); 6 had a PVC pipe ring spacer filling the gap (PCV group 2). Animals-large breed cadaveric canine cervical vertebral columns (C2-C7) from skeletally mature dogs without (cadaveric group 1, n = 6, historical data) and with an intervertebral disk spacer (cadaveric group 2, n = 6) were used. All PVC models and cadaver specimens were instrumented with monocortical titanium screws/PMMA. Stiffness of the 2 PVC groups was compared in extension, flexion, and lateral bending using non-destructive 4-point bend testing. Stiffness testing in all 3 directions was performed of the unaltered C4-C5 vertebral motion unit in cadaveric spines and repeated after placement of an intervertebral cortical allograft ring and instrumentation. Data were compared using a linear mixed model approach that also incorporated data from previously tested spines with the same screw/PMMA construct but without disk spacer (cadaveric group 1). RESULTS Addition of a spacer increased construct stiffness in both the PVC model (P < .001) and cadaveric vertebral columns (P < .001) compared to fixation without a spacer. CONCLUSIONS Addition of an intervertebral spacer significantly increased construct stiffness of monocortical screw/PMMA fixation.

Determinants of CD4 cell count decline after treatment interruption

Patients who had started HAART (Highly Active Anti-Retroviral Treatment) under previous aggressive DHHS guidelines (1997) underwent a life-long continuous HAART that was associated with many short term as well as long term complications. Many interventions attempted to reduce those complications including intermittent treatment also called pulse therapy. Many studies were done to study the determinants of rate of fall in CD4 count after interruption as this data would help guide treatment interruptions. The data set used here was a part of a cohort study taking place at the Johns Hopkins AIDS service since January 1984, in which the data were collected both prospectively and retrospectively. The patients in this data set consisted of 47 patients receiving via pulse therapy with the aim of reducing the long-term complications. ^ The aim of this project was to study the impact of virologic and immunologic factors on the rate of CD4 loss after treatment interruption. The exposure variables under investigation included CD4 cell count and viral load at treatment initiation. The rates of change of CD4 cell count after treatment interruption was estimated from observed data using advanced longitudinal data analysis methods (i.e., linear mixed model). Using random effects accounted for repeated measures of CD4 per person after treatment interruption. The regression coefficient estimates from the model was then used to produce subject specific rates of CD4 change accounting for group trends in change. The exposure variables of interest were age, race, and gender, CD4 cell counts and HIV RNA levels at HAART initiation. ^ The rate of fall of CD4 count did not depend on CD4 cell count or viral load at initiation of treatment. Thus these factors may not be used to determine who can have a chance of successful treatment interruption. CD4 and viral load were again studied by t-tests and ANOVA test after grouping based on medians and quartiles to see any difference in means of rate of CD4 fall after interruption. There was no significant difference between the groups suggesting that there was no association between rate of fall of CD4 after treatment interruption and above mentioned exposure variables. ^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

Comparación a campo de distintos cultivares de olivo (Olea europaea L.) como hospedadores de Siphoninus phillyreae (Haliday)

La mosca blanca del fresno, Siphoninus phillyreae (Haliday) , es una especie polífaga invasiva que causa graves daños en sus hospedadores, entre ellos el olivo (Olea europaea L.). El uso del hospedador por parte de S. phillyreae fue observado en tres cultivares de olivo (Arauco, Arbequina y Aloreña) en el norte de la provincia de La Rioja (Argentina) durante 2007 y 2008. De cada cultivar se muestrearon seis plantas infestadas y de cada planta se tomaron ocho hojas. De cada hoja se registró la abundancia de adultos y estados inmaduros de la mosca blanca que fueron comparadas entre los cultivares mediante modelos lineales generales y mixtos. Los resultados obtenidos mostraron diferencias significativas entre las densidades de adultos y ninfas de S. phillyreae en los distintos cultivares de olivo analizados. Dichos resultados indican que este insecto realiza un uso diferente de los cultivares de olivo, siendo Arauco y Arbequina las variedades hospedadoras más utilizadas en la zona de estudio.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.