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In a stratified analysis, the results from different strata if homogeneity assumption is met are pooled together to obtain a single summary estimate for the common effect parameter. However, the effect can appear homogeneous across strata using one measure but heterogeneous using another. Consequently, two researchers analyzing the same data can arrive at conflicting conclusions if they use different effect measures. In this paper, the author draws on the sufficient component cause model to develop a stratified-analysis method regarding a particular effect measure, the ‘peril ratio’. When there is no mechanistic interaction between the exposure under study and the stratifying variable (i.e., when they do not work together to complete any sufficient cause), the peril ratio is constant across strata. The author presents formulas for the estimation of such a common peril ratio. Three real data are re-analyzed for illustration. When the data is consistent with peril-ratio homogeneity in a stratified analysis, researchers can use the formulas in this paper to pool the strata. [ABSTRACT FROM AUTHOR]

In genetic association studies, tests for Hardy-Weinberg proportions are often employed as a quality control checking procedure. Missing genotypes are typically discarded prior to testing. In this paper we show that inference for Hardy-Weinberg proportions can be biased when missing values are discarded. We propose to use multiple imputation of missing values in order to improve inference for Hardy-Weinberg proportions. For imputation we employ a multinomial logit model that uses information from allele intensities and/or neighbouring markers. Analysis of an empirical data set of single nucleotide polymorphisms possibly related to colon cancer reveals that missing genotypes are not missing completely at random. Deviation from Hardy-Weinberg proportions is mostly due to a lack of heterozygotes. Inbreeding coefficients estimated by multiple imputation of the missings are typically lowered with respect to inbreeding coefficients estimated by discarding the missings. Accounting for missings by multiple imputation qualitatively changed the results of 10 to 17% of the statistical tests performed. Estimates of inbreeding coefficients obtained by multiple imputation showed high correlation with estimates obtained by single imputation using an external reference panel. Our conclusion is that imputation of missing data leads to improved statistical inference for Hardy-Weinberg proportions. [ABSTRACT FROM AUTHOR]

Statistical Parametric Mapping (SPM) is the dominant paradigm for mass-univariate analysis of neuroimaging data. More recently, a Bayesian approach termed Posterior Probability Mapping (PPM) has been proposed as an alternative. PPM offers two advantages: (i) inferences can be made about effect size thus lending a precise physiological meaning to activated regions, (ii) regions can be declared inactive. This latter facility is most parsimoniously provided by PPMs based on Bayesian model comparisons. To date these comparisons have been implemented by an Independent Model Optimization (IMO) procedure which separately fits null and alternative models. This paper proposes a more computationally efficient procedure based on Savage-Dickey approximations to the Bayes factor, and Taylor-series approximations to the voxel-wise posterior covariance matrices. Simulations show the accuracy of this Savage-Dickey-Taylor (SDT) method to be comparable to that of IMO. Results on fMRI data show excellent agreement between SDT and IMO for second-level models, and reasonable agreement for first-level models. This Savage-Dickey test is a Bayesian analogue of the classical SPM-F and allows users to implement model comparison in a truly interactive manner. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis. [ABSTRACT FROM AUTHOR]

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. [ABSTRACT FROM AUTHOR]

Graph theoretical approaches have successfully revealed abnormality in brain connectivity, in particular, for contrasting patients from healthy controls. Besides the group comparison analysis, a correlational study is also challenging. In studies with patients, for example, finding brain connections that indeed deepen specific symptoms is interesting. The correlational study is also beneficial since it does not require controls, which are often difficult to find, especially for old-age patients with cognitive impairment where controls could also have cognitive deficits due to normal ageing. However, one of the major difficulties in such correlational studies is too conservative multiple comparison correction. In this paper, we propose a novel method for identifying brain connections that are correlated with a specific cognitive behavior by employing cluster-based statistics, which is less conservative than other methods, such as Bonferroni correction, false discovery rate procedure, and extreme statistics. Our method is based on the insight that multiple brain connections, rather than a single connection, are responsible for abnormal behaviors. Given brain connectivity data, we first compute a partial correlation coefficient between every edge and the behavioral measure. Then we group together neighboring connections with strong correlation into clusters and calculate their maximum sizes. This procedure is repeated for randomly permuted assignments of behavioral measures. Significance levels of the identified sub-networks are estimated from the null distribution of the cluster sizes. This method is independent of network construction methods: either structural or functional network can be used in association with any behavioral measures. We further demonstrated the efficacy of our method using patients with subcortical vascular cognitive impairment. We identified sub-networks that are correlated with the disease severity by exploiting diffusion tensor imaging techniques. The identified sub-networks were consistent with the previous clinical findings having valid significance level, while other methods did not assert any significant findings. [ABSTRACT FROM AUTHOR]

This work aims at identifying a set of humoral immunologic parameters that improve prediction of the activation process in HIV patients. Starting from the well-known impact of humoral immunity in HIV infection, there is still a lack of knowledge in defining the role of the modulation of functional activity and titers of serum antibodies from early stage of infection to the development of AIDS. We propose an integrated approach that combines humoral and clinical parameters in defining the host immunity, implementing algorithms associated with virus control. A number of humoral parameters were simultaneously evaluated in a whole range of serum samples from HIV-positive patients. This issue has been afforded accounting for estimation problems typically related to “feasibility” studies where small sample size in each group and large number of parameters are jointly estimated. We used nonparametric statistical procedures to identify biomarkers in our study which included 42 subjects stratified on five different stages of HIV infection, i.e., Elite Controllers (EC), Long Term Non Progressors (LTNP), HAART, AIDS and Acute Infection (AI). The main goal of the paper is to illustrate a novel profiling method for helping to design a further confirmatory study. A set of seventeen different HIV-specific blood humoral factors were analyzed in all subjects, i.e. IgG and IgA to gp120IIIB, to gp120Bal, to whole gp41, to P1 and T20 gp41 epitopes of the MPER-HR2 region, to QARILAV gp41 epitope of the HR1 region and to CCR5; neutralization activity against five different virus strains and ADCC were also evaluated. Patients were selected on the basis of CD4 cell counts, HIV/RNA and clinical status. The Classification and Regression Trees (CART) approach has been used to uncover specific patterns of humoral parameters in different stages of HIV disease. Virus neutralization of primary virus strains and antibodies to gp41 were required to classify patients, suggesting that clinical profiles strongly rely on functional activity against HIV. [ABSTRACT FROM AUTHOR]

We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical. [ABSTRACT FROM AUTHOR]

Antigenic characterization based on serological data, such as Hemagglutination Inhibition (HI) assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from a number of individual experiments. Measurements from different experiments may be inconsistent due to different experimental conditions. Consequently we will observe a matrix with missing data and possibly inconsistent measurements. In this paper, we develop a new mathematical model, which we refer to as Joint Matrix Completion and Filtering, for HI data integration. In this approach, we simultaneously handle the incompleteness and uncertainty of observations by assuming that the underlying merged HI data matrix has low rank, as well as carefully modeling different levels of noises in each individual table. An efficient blockwise coordinate descent procedure is developed for optimization. The performance of our approach is validated on synthetic and real influenza datasets. The proposed joint matrix completion and filtering model can be adapted as a general model for biological data integration, targeting data noises and missing values within and across experiments. [ABSTRACT FROM AUTHOR]

The objectives of this paper are to predict life years lost associated with obesity-related diseases (ORDs) for U.S. non-smoking adults, and to examine the relationship between those ORDs and mortality. Data from the National Health Interview Survey, 1997–2000, were used. We employed mixed proportional hazard models to estimate the association between those ORDs and mortality and used simulations to project life years lost associated with the ORDs. We found that obesity-attributable comorbidities are associated with large decreases in life years and increases in mortality rates. The life years lost associated with ORDs is more marked for younger adults than older adults, for blacks than whites, for males than females, and for the more obese than the less obese. Using U.S. non-smoking adults aged 40 to 49 years as an example to illustrate percentage of the life years lost associated with ORDs, we found that the mean life years lost associated with ORDs for U.S. non-smoking black males aged 40 to 49 years with a body mass index above 40 kg/m2 was 5.43 years, which translates to a 7.5% reduction in total life years. White males of the same age range and same degree of obesity lost 5.23 life years on average – a 6.8% reduction in total life years, followed by black females (5.04 years, a 6.5% reduction in life years), and white females (4.7 years, a 5.8% reduction in life years). Overall, ORDs increased chances of dying and lessened life years by 0.2 to 11.7 years depending on gender, race, BMI classification, and age. [ABSTRACT FROM AUTHOR]

Objective: To use multilevel functional principal component analysis to exploit the information inherent in the shape of longitudinally sampled glucose curves during pregnancy, and to analyse the impact of glucose curve characteristics on neonatal birth weight, percentage fat and cord blood C-peptide. Study Design and Setting: A cohort study of healthy, pregnant women (n = 884). They underwent two oral glucose tolerance tests (gestational weeks 14–16 and 30–32), which gave two glucose curves per woman. Results: Glucose values were higher, and peaked later in third trimester than in early pregnancy. The curve characteristic “general glucose level” accounted for 91% of the variation across visits, and 72% within visits. The curve characteristics “timing of postprandial peak”, and “oscillating glucose levels” accounted for a larger part of the variation within visits (15% and 8%), than across visits (7% and <2%). A late postprandial peak during pregnancy, and high general glucose levels in third trimester had significant, positive effects on birth weight (p<0.05). Generally high glucose levels during pregnancy had a significant, positive impact on neonatal percentage fat (p = 0.04). High general glucose level in third trimester had a significant, positive impact on cord blood C-peptide (p = 0.004). Conclusion: Shape information in entire OGTT curves provides significant physiological information of importance for several outcomes, and may contribute to the understanding of the metabolic changes during pregnancy. [ABSTRACT FROM AUTHOR]

Objective: Patients treated for nonfunctioning pituitary macroadenoma (NFMA) with suprasellar extension show disturbed sleep characteristics, possibly related to hypothalamic dysfunction. In addition to hypopituitarism, both structural hypothalamic damage and sleep restriction per se are associated with the metabolic syndrome. However, the prevalence of the metabolic syndrome in patients with NFMA is not well established. Our objective was to study the prevalence and risk factors for (components of) the metabolic syndrome in patients treated for NFMA. Design: The metabolic syndrome (NCEP-ATP III criteria) was studied in an unselected cohort of 145 NFMA patients (aged 26–88yr, 44% female) in long-term remission after treatment, receiving adequate stable hormone replacement for any pituitary deficiencies. The results were compared to population data of 63,995 Dutch inhabitants by standardization (LifeLines cohort study). Results: NFMA patients showed increased risk for reduced HDL-cholesterol (SMR 1.59, 95% CI 1.13–2.11), increased triglyceride levels (SMR 2.31, 95% CI 1.78–2.90) and the metabolic syndrome (SMR 1.60, 95% CI 1.22–2.02), but not for increased blood pressure, waist circumference or hyperglycemia. Preoperative visual field defects independently affected the risk for increased blood pressure (OR 6.5, 95% CI 1.9–22.2), and hypopituitarism was associated with a body mass index - dependent risk for increased waist circumference (OR 1.6, 95% CI 1.2–2.2) and the metabolic syndrome (OR 1.4, 95% CI 1.0–1.9). Conclusions: Patients treated for NFMA are increased at risk for developing the metabolic syndrome, mainly due to decreased HDL-cholesterol and increased triglycerides. Risk factors included hypopituitarism and preoperative visual field defects. Hypothalamic dysfunction may explain the metabolic abnormalities, in addition to intrinsic imperfections of hormone replacement therapy. Additional research is required to explore the relation between derangements in circadian rhythmicity and metabolic syndrome in these patients. [ABSTRACT FROM AUTHOR]

One major drawback of deception detection is its vulnerability to countermeasures, whereby participants wilfully modulate their physiological or neurophysiological response to critical guilt-determining stimuli. One reason for this vulnerability is that stimuli are usually presented slowly. This allows enough time to consciously apply countermeasures, once the role of stimuli is determined. However, by increasing presentation speed, stimuli can be placed on the fringe of awareness, rendering it hard to perceive those that have not been previously identified, hindering the possibility to employ countermeasures. We tested an identity deception detector by presenting first names in Rapid Serial Visual Presentation and instructing participants to lie about their own identity. We also instructed participants to apply a series of countermeasures. The method proved resilient, remaining effective at detecting deception under all countermeasures. [ABSTRACT FROM AUTHOR]

Chronic subdural hematoma (CSDH) is a relatively common disorder in neurosurgery on elderly patients, though the mechanism that causes the disease remains unclear. Studies have suggested that local anticoagulation and inflammatory changes may be important in its pathogenesis. Most studies have used a basic bivariate statistical analysis to assess complex immunological responses in patients with this disorder, hence a more sophisticated multivariate statistical approach might be warranted. Our objective was to assess the association and correlation between the pro- and anti-inflammatory responses in a cohort of patients with chronic subdural hematoma (n = 57) using an exploratory and confirmatory factor analysis. Thirteen assigned pro-inflammatory (TNF-α, IL-1β, IL-2, IL-2R, IL-6, IL-7, IL-12, IL-15, IL-17, CCL2, CXCL8, CXCL9 and CXCL10) and five assigned anti-inflammatory (IL-1RA, IL-4, IL-5, IL-10 and IL-13) cytokines from blood and hematoma fluid samples were examined. Exploratory factor analysis indicated two major underlying immunological processes expressed by the cytokines in both blood and hematoma fluid, but with a different pattern and particularly regarding the cytokines IL-13, IL-6, IL-4 and TNF-α. Scores from confirmatory factor analysis models exhibited a higher correlation between pro- and anti-inflammatory activities in blood (r  = 0.98) than in hematoma fluid samples (r  = 0.92). However, correlations of inflammatory processes between blood and hematoma fluid samples were lower and non-significant. A structural equation model showed a significant association between increased anti-inflammatory activity in hematoma fluid samples and a lower risk of recurrence, but this relationship was not statistically significant in venous blood samples. Moreover, these findings indicate that anti-inflammatory activities in the hematoma may play a role in the risk of a recurrence of CSDH. [ABSTRACT FROM AUTHOR]

Background: Due to the lack of timely access to resources for critical care, strategic use of antiviral drugs is crucial for mitigating the impact of novel influenza viruses with pandemic potential in remote and isolated communities. We sought to evaluate the effect of antiviral treatment and prophylaxis of close contacts in a Canadian remote northern community. Methods: We used an agent-based, discrete-time simulation model for disease spread in a remote community, which was developed as an in-silico population using population census data. Relative and cumulative age-specific attack rates, and the total number of infections in simulated model scenarios were obtained. Results: We found that early initiation of antiviral treatment is more critical for lowering attack rates in a remote setting with a low population-average age compared to an urban population. Our results show that a significant reduction in the relative, age-specific attack rates due to increasing treatment coverage does not necessarily translate to a significant reduction in the overall arrack rate. When treatment coverage varies from low to moderate, targeted prophylaxis has a very limited impact in reducing attack rates and should be offered at a low level (below 10%) to avoid excessive waste of drugs. Conclusions: In contrast to previous work, for conservative treatment coverages, our results do not provide any convincing evidence for the implementation of targeted prophylaxis. The findings suggest that public health strategies in remote communities should focus on the wider availability (higher coverage) and timely distribution of antiviral drugs for treatment of clinically ill individuals. [ABSTRACT FROM AUTHOR]

Antrodia cinnamomea is a traditional healthy food that has been demonstrated to possess anti-inflammatory, antioxidative, and anticacer effects. The purpose of this study was to evaluate whether the ethanolic extract of A. cinnamomea (EEAC) can affect the efflux function of P-glycoprotein (P-gp) and the effect of ABCB1 genetic variants on the interaction between EEAC and P-gp. To investigate the mechanism of this interaction, Flp-In™-293 cells stably transfected with various genotypes of human P-gp were established and the expression of P-gp was confirmed by Western blot. The results of the rhodamine 123 efflux assay demonstrated that EEAC efficiently inhibited wild-type P-gp function at an IC50 concentration of 1.51±0.08 µg/mL through non-competitive inhibition. The IC50 concentrations for variant-type 1236T-2677T-3435T P-gp and variant-type 1236T-2677A-3435T P-gp were 5.56±0.49 µg/mL and 3.33±0.67 µg/mL, respectively. In addition, the inhibition kinetics of EEAC also changed to uncompetitive inhibition in variant-type 1236T-2677A-3435T P-gp. The ATPase assay revealed that EEAC was an ATPase stimulator and was capable of reducing verapamil-induced ATPase levels. These results indicate that EEAC may be a potent P-gp inhibitor and higher dosages may be required in subjects carrying variant-types P-gp. Further studies are required to translate this basic knowledge into clinical applications. [ABSTRACT FROM AUTHOR]

Understanding the impact of multiple genetic variants and their interactions on the disease penetrance of familial multiple prostate cancer is very relevant to the overall understanding of carcinogenesis. We assessed the joint effect of two loci on rs4242382 at 8q24 and rs10486567 at 7p15.2 to this end. We analyzed the data from a Finnish family-based genetic study, which was composed of 947 men including 228 cases in 75 families, to evaluate the respective effects of the two loci on the disease penetrance; in particular, the occurrence and number of prostate cancer cases within a family were utilized to evaluate the interactions between the two loci under the additive and multiplicative Poisson regression models. The risk alleles A at rs4242382 (OR = 1.14, 95% CI 1.08–1.19, P<0.0001) and a risk allele A at rs10486567 (OR = 1.06, 96%CI 1.01–1.11, P = 0.0208) were found to be associated with an increased risk of familial PrCa, especially with four or more cases within a family. A multiplicative model fitted the joint effect better than an additive model (likelihood ratio test X2 = 13.89, P<0.0001). The influence of the risk allele A at rs10486567 was higher in the presence of the risk allele A at rs4242382 (OR = 1.09 (1.01–1.18) vs. 1.01 (0.95–1.07)). Similar findings were observed in non-aggressive PrCa, but not in aggressive PrCa. We demonstrated that two loci (rs4242382 and rs10486567) are highly associated with familial multiple PrCa, and the gene-gene interaction or statistical epistasis was consistent with the Fisher's multiplicative model. These loci's association and epistasis were observed for non-aggressive but not for aggressive tumors. The proposed statistical model can be further developed to accommodate multi-loci interactions to provide further insights into epistasis. [ABSTRACT FROM AUTHOR]

Background: In healthy individuals slow temporal summation of pain or wind-up (WU) can be evoked by repetitive heat-pulses at frequencies of ≥.33 Hz. Previous WU studies have used various stimulus frequencies and intensities to characterize central sensitization of human subjects including fibromyalgia (FM) patients. However, many trials demonstrated considerable WU-variability including zero WU or even wind-down (WD) at stimulus intensities sufficient for activating C-nociceptors. Additionally, few WU-protocols have controlled for contributions of individual pain sensitivity to WU-magnitude, which is critical for WU-comparisons. We hypothesized that integration of 3 different WU-trains into a single WU-response function (WU-RF) would not only control for individuals’ pain sensitivity but also better characterize their central pain responding including WU and WD. Methods: 33 normal controls (NC) and 38 FM patients participated in a study of heat-WU. We systematically varied stimulus intensities of.4 Hz heat-pulse trains applied to the hands. Pain summation was calculated as difference scores of 1st and 5th heat-pulse ratings. WU-difference (WU-Δ) scores related to 3 heat-pulse trains (44°C, 46°C, 48°C) were integrated into WU-response functions whose slopes were used to assess group differences in central pain sensitivity. WU-aftersensations (WU-AS) at 15 s and 30 s were used to predict clinical FM pain intensity. Results: WU-Δ scores linearly accelerated with increasing stimulus intensity (p<.001) in both groups of subjects (FM>NC) from WD to WU. Slope of WU-RF, which is representative of central pain sensitivity, was significantly steeper in FM patients than NC (p<.003). WU-AS predicted clinical FM pain intensity (Pearson’s r = .4; p<.04). Conclusions: Compared to single WU series, WU-RFs integrate individuals’ pain sensitivity as well as WU and WD. Slope of WU-RFs was significantly different between FM patients and NC. Therefore WU-RF may be useful for assessing central sensitization of chronic pain patients in research and clinical practice. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the relationships of intrahepatic cccDNA with serum HBsAg and with HBV DNA in treatment-naive patients throughout acute and chronic HBV infection. Methods: A total of 120 patients who had a liver biopsy were enrolled, including 19 with acute hepatitis B (AHB), and 101 patients with chronic HBV infection (CHB) of whom were 10 in immune-tolerant (IT) phase, 59 in immune-clearance (IC) phase, 8 in low-replicative (LR) phase, and 24 in HBeAg-negative hepatitis (ENH) phase. Intrahepatic cccDNA, serum HBsAg and serum HBV DNA levels were comparatively analyzed. Results: The median intrahepatic cccDNA levels were 0.18 4.80, 3.81, 0.22 and 0.97 copies/cell for patients with AHB, CHB-IT, CHB-IC, CHB-LR, and CHB-ENH, respectively. In AHB patients, intrahepatic cccDNA was positively correlated with serum HBsAg (r = 0.665, P = 0.003), as well as serum HBV DNA (r = 0.536, P = 0.022). In CHB patients, intrahepatic cccDNA was positively correlated with serum HBsAg in the IC phase (r = 0.392, P = 0.005), and with serum HBV DNA in the IC phase (r = 0.301, P = 0.036) and ENH phase (r = 0.588, P = 0.013). HBV replicative efficiency, defined as the ratio of serum HBV DNA to intrahepatic cccDNA, was obviously lower in AHB and CHB-LR patients than in CHB-IT, CHB-IC and CHB-ENH patients (0.70 and 0.53 vs. 1.12, 1.09 and 0.99, P<0.001, values were logarithmic transformed for analysis). In CHB-IC patients, HBV replicative efficiency was positively correlated with histological activity index of liver inflammation (r = 0.308, P = 0.009). Conclusion: Serum HBsAg and HBV DNA levels may reflect the amount of active intrahepatic cccDNA in treatment-naive AHB and CHB-IC patients. Reduced intrahepatic cccDNA and HBV replicative efficiency may imply effective immune control of HBV infection. [ABSTRACT FROM AUTHOR]

Background: Evidence regarding the mortality rate after administration of the pandemic influenza A (H1N1) 2009 vaccine on patients with underlying diseases is currently scarce. We conducted a case-control study in Japan to compare the mortality rates of patients with idiopathic interstitial pneumonia after the vaccines were administered and were not administered. Methods: Between October 2009 and March 2010, we collected clinical records in Japan and conducted a 1∶1 matched case-control study. Patients with idiopathic interstitial pneumonia who died during this period were considered case patients, and those who survived were considered control patients. We determined and compared the proportion of each group that received the pandemic influenza A (H1N1) 2009 vaccine and estimated the odds ratio. Finally, we conducted simulations that compensated for the shortcomings of the study associated with adjusted severity of idiopathic interstitial pneumonia. Results: The case and control groups each comprised of 75 patients with idiopathic interstitial pneumonia. The proportion of patients who received the pandemic influenza A (H1N1) 2009 vaccine was 30.7% and 38.7% for the case and control groups, respectively. During that winter, the crude conditional odds ratio of mortality was 0.63 (95% confidence interval, 0.25–1.47) and the adjusted conditional odds ratio was 1.18 (95% confidence interval, 0.33–4.49); neither was significant. The simulation study showed more accurate conditional odds ratios of 0.63–0.71. Conclusions: In our study, we detected no evidence that the influenza A (H1N1) 2009 vaccine increased the mortality rate of patients with idiopathic interstitial pneumonia. The results, however, are limited by the small sample size and low statistical power. A larger-scale study is required. [ABSTRACT FROM AUTHOR]

Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference requires enough flexibility of the correlation model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the correlation structure. For many one-dimensional spatial or temporal arrays, the linear exponent autoregressive (LEAR) correlation structure meets these two opposing goals in one model. The LEAR structure is a flexible two-parameter correlation model that applies to situations in which the within-subject correlation decreases exponentially in time or space. It allows for an attenuation or acceleration of the exponential decay rate imposed by the commonly used continuous-time AR(1) structure. We propose the Kronecker product LEAR correlation structure for multivariate repeated measures data in which the correlation between measurements for a given subject is induced by two factors (e.g., spatial and temporal dependence). Excellent analytic and numerical properties make the Kronecker product LEAR model a valuable addition to the suite of parsimonious correlation structures for multivariate repeated measures data. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the appeal of the Kronecker product LEAR correlation structure. [ABSTRACT FROM AUTHOR]

Peptide probes for imaging retinal ganglion cell (RGC) apoptosis consist of a cell-penetrating peptide targeting moiety and a fluorophore-quencher pair flanking an effector caspase consensus sequence. Using ex vivo fluorescence imaging, we previously validated the capacity of these probes to identify apoptotic RGCs in cell culture and in an in vivo rat model of N-methyl- D-aspartate (NMDA)-induced neurotoxicity. Herein, using TcapQ488, a new probe designed and synthesized for compatibility with clinically-relevant imaging instruments, and real time imaging of a live rat RGC degeneration model, we fully characterized time- and dose-dependent probe activation, signal-to-noise ratios, and probe safety profiles in vivo. Adult rats received intravitreal injections of four NMDA concentrations followed by varying TcapQ488 doses. Fluorescence fundus imaging was performed sequentially in vivo using a confocal scanning laser ophthalmoscope and individual RGCs displaying activated probe were counted and analyzed. Rats also underwent electroretinography following intravitreal injection of probe. In vivo fluorescence fundus imaging revealed distinct single-cell probe activation as an indicator of RGC apoptosis induced by intravitreal NMDA injection that corresponded to the identical cells observed in retinal flat mounts of the same eye. Peak activation of probe in vivo was detected 12 hours post probe injection. Detectable fluorescent RGCs increased with increasing NMDA concentration; sensitivity of detection generally increased with increasing TcapQ488 dose until saturating at 0.387 nmol. Electroretinography following intravitreal injections of TcapQ488 showed no significant difference compared with control injections. We optimized the signal-to-noise ratio of a caspase-activatable cell penetrating peptide probe for quantitative non-invasive detection of RGC apoptosis in vivo. Full characterization of probe performance in this setting creates an important in vivo imaging standard for functional evaluation of future probe analogues and provides a basis for extending this strategy into glaucoma-specific animal models. [ABSTRACT FROM AUTHOR]

Osteoporosis is characterized by reduced bone mineral density (BMD) and increased fracture risk. Fat mass is a determinant of bone strength and both phenotypes have a strong genetic component. In this study, we examined the association between obesity associated polymorphisms (SNPs) with body composition, BMD, Ultrasound (QUS), fracture and biomarkers (Homocysteine (Hcy), folate, Vitamin D and Vitamin B12) for obesity and osteoporosis. Five common variants: rs17782313 and rs1770633 (melanocortin 4 receptor (MC4R); rs7566605 (insulin induced gene 2 (INSIG2); rs9939609 and rs1121980 (fat mass and obesity associated (FTO) were genotyped in 2 cohorts of Swedish women: PEAK-25 (age 25, n = 1061) and OPRA (age 75, n = 1044). Body mass index (BMI), total body fat and lean mass were strongly positively correlated with QUS and BMD in both cohorts (r2 = 0.2–0.6). MC4R rs17782313 was associated with QUS in the OPRA cohort and individuals with the minor C-allele had higher values compared to T-allele homozygotes (TT vs. CT vs. CC: BUA: 100 vs. 103 vs. 103; p = 0.002); (SOS: 1521 vs. 1526 vs. 1524; p = 0.008); (Stiffness index: 69 vs. 73 vs. 74; p = 0.0006) after adjustment for confounders. They also had low folate (18 vs. 17 vs. 16; p = 0.03) and vitamin D (93 vs. 91 vs. 90; p = 0.03) and high Hcy levels (13.7 vs 14.4 vs. 14.5; p = 0.06). Fracture incidence was lower among women with the C-allele, (52% vs. 58%; p = 0.067). Variation in MC4R was not associated with BMD or body composition in either OPRA or PEAK-25. SNPs close to FTO and INSIG2 were not associated with any bone phenotypes in either cohort and FTO SNPs were only associated with body composition in PEAK-25 (p≤0.001). Our results suggest that genetic variation close to MC4R is associated with quantitative ultrasound and risk of fracture. [ABSTRACT FROM AUTHOR]

The one-size-fits-all paradigm in organized screening of breast cancer is shifting towards a personalized approach. The present study has two objectives: 1) To perform an economic evaluation and to assess the harm-benefit ratios of screening strategies that vary in their intensity and interval ages based on breast cancer risk; and 2) To estimate the gain in terms of cost and harm reductions using risk-based screening with respect to the usual practice. We used a probabilistic model and input data from Spanish population registries and screening programs, as well as from clinical studies, to estimate the benefit, harm, and costs over time of 2,624 screening strategies, uniform or risk-based. We defined four risk groups, low, moderate-low, moderate-high and high, based on breast density, family history of breast cancer and personal history of breast biopsy. The risk-based strategies were obtained combining the exam periodicity (annual, biennial, triennial and quinquennial), the starting ages (40, 45 and 50 years) and the ending ages (69 and 74 years) in the four risk groups. Incremental cost-effectiveness and harm-benefit ratios were used to select the optimal strategies. Compared to risk-based strategies, the uniform ones result in a much lower benefit for a specific cost. Reductions close to 10% in costs and higher than 20% in false-positive results and overdiagnosed cases were obtained for risk-based strategies. Optimal screening is characterized by quinquennial or triennial periodicities for the low or moderate risk-groups and annual periodicity for the high-risk group. Risk-based strategies can reduce harm and costs. It is necessary to develop accurate measures of individual risk and to work on how to implement risk-based screening strategies. [ABSTRACT FROM AUTHOR]

Multivariate analytical routines have become increasingly popular in the study of cerebral function in health and in disease states. Spatial covariance analysis of functional neuroimaging data has been used to identify and validate characteristic topographies associated with specific brain disorders. Voxel-wise correlations can be used to assess similarities and differences that exist between covariance topographies. While the magnitude of the resulting topographical correlations is critical, statistical significance can be difficult to determine in the setting of large data vectors (comprised of over 100,000 voxel weights) and substantial autocorrelation effects. Here, we propose a novel method to determine the p-value of such correlations using pseudo-random network simulations. [ABSTRACT FROM AUTHOR]

Pregnant women carry a mixture of cell-free DNA fragments from self and fetus (non-self) in their circulation. In recent years multiple independent studies have demonstrated the ability to detect fetal trisomies such as trisomy 21, the cause of Down syndrome, by Next-Generation Sequencing of maternal plasma. The current clinical tests based on this approach show very high sensitivity and specificity, although as yet they have not become the standard diagnostic test. Here we describe improvements to the analysis of the sequencing data by reducing GC bias and better handling of the genomic repeats. We show substantial improvements in the sensitivity of the standard trisomy 21 statistical tests, which we measure by artificially reducing read coverage. We also explore the bias stemming from the natural cleavage of plasma DNA by examining DNA motifs and position specific base distributions. We propose a model to correct this fragmentation bias and observe that incorporating this bias does not lead to any further improvements in the detection of fetal trisomy. The improved bias corrections that we demonstrate in this work can be readily adopted into existing fetal trisomy detection protocols and should also lead to improvements in sub-chromosomal copy number variation detection. [ABSTRACT FROM AUTHOR]

Identification of risk factors in patients with a particular disease can be analyzed in clinical data sets by using feature selection procedures of pattern recognition and data mining methods. The applicability of the relaxed linear separability (RLS) method of feature subset selection was checked for high-dimensional and mixed type (genetic and phenotypic) clinical data of patients with end-stage renal disease. The RLS method allowed for substantial reduction of the dimensionality through omitting redundant features while maintaining the linear separability of data sets of patients with high and low levels of an inflammatory biomarker. The synergy between genetic and phenotypic features in differentiation between these two subgroups was demonstrated. [ABSTRACT FROM AUTHOR]

Renal α-Klotho (α-KL) plays a fundamental role as a co-receptor for fibroblast growth factor 23 (FGF23), a phosphaturic hormone and regulator of 1,25(OH)2 vitamin D3 (1,25VitD3). Disruption of FGF23-α-KL signaling is thought to be an early hallmark of chronic kidney disease (CKD) involving reduced renal α-KL expression and a reciprocal rise in serum FGF23. It remains unclear, however, whether the rise in FGF23 is related to the loss of renal α-KL. We evaluated α-KL expression in renal biopsy samples and measured levels of several parameters of mineral metabolism, as well as soluble α-KL (sKL), in serum and urinary samples from CKD patients (n = 236). We found that although renal α-KL levels were significantly reduced and serum FGF23 levels were significantly elevated in early and intermediate CKD, serum phosphate levels remained within the normal range. Multiple regression analysis showed that the increases in FGF23 were significantly associated with reduced renal function and elevated serum phosphate, but were not associated with loss of renal α-KL. Moreover, despite falling renal α-KL levels, the increase in FGF23 enhanced urinary fractional excretion of phosphate and reduced serum 1,25VitD3 levels in early and intermediate CKD, though not in advanced CKD. Serum sKL levels also fell significantly over the course of CKD, and renal α-KL was a significant independent determinant of sKL. These results demonstrate that FGF23 levels rise to compensate for renal failure-related phosphate retention in early and intermediate CKD. This enables FGF23-α-KL signaling and a neutral phosphate balance to be maintained despite the reduction in α-KL. In advanced CKD, however, renal α-KL declines further. This disrupts FGF23 signaling, and serum phosphate levels significantly increase, stimulating greater FGF23 secretion. Our results also suggest the serum sKL concentration may be a useful marker of renal α-KL expression levels. [ABSTRACT FROM AUTHOR]

Background: Analysis of potentially different impact of Lopinavir/Ritonavir (LPV/r) on non-B subtypes is confounded by dissimilarities in the conditions existing in different countries. We retrospectively compared its impact on populations infected with subtypes B and C in Israel, where patients infected with different subtypes receive the same treatment. Methods: Clinical and demographic data were reported by physicians. Resistance was tested after treatment failure. Statistical analyses were conducted using SPSS. Results: 607 LPV/r treated patients (365 male) were included. 139 had HIV subtype B, 391 C, and 77 other subtypes. At study end 429 (71%) were receiving LPV/r. No significant differences in PI treatment history and in median viral-load (VL) at treatment initiation and termination existed between subtypes. MSM discontinued LPV/r more often than others even when the virologic outcome was good (p = 0.001). VL was below detection level in 81% of patients for whom LPV/r was first PI and in 67% when it was second (P = 0.001). Median VL decrease from baseline was 1.9±0.1 logs and was not significantly associated with subtype. Median CD4 increase was: 162 and 92cells/µl, respectively, for patients receiving LPV/r as first and second PI (P = 0.001), and 175 and 98, respectively, for subtypes B and C (P<0.001). Only 52 (22%) of 237 patients genotyped while under LPV/r were fully resistant to the drug; 12(5%) were partially resistant. In48%, population sequencing did not reveal resistance to any drug notwithstanding the virologic failure. No difference was found in the rates of resistance development between B and C (p = 0.16). Conclusions: Treatment with LPV/r appeared efficient and tolerable in both subtypes, B and C, but CD4 recovery was significantly better in virologically suppressed subtype-B patients. In both subtypes, LPV/r was more beneficial when given as first PI. Mostly, reasons other than resistance development caused discontinuation of treatment. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) are key regulatory molecules involved in a variety of biological processes and human diseases. However, the pathological effects of lncRNAs on primary varicose great saphenous veins (GSVs) remain unclear. The purpose of the present study was to identify aberrantly expressed lncRNAs involved in the prevalence of GSV varicosities and predict their potential functions. Using microarray with 33,045 lncRNA and 30,215 mRNA probes, 557 lncRNAs and 980 mRNAs that differed significantly in expression between the varicose great saphenous veins and control veins were identified in six pairs of samples. These lncRNAs were sub-grouped and mRNAs expressed at different levels were clustered into several pathways with six focused on metabolic pathways. Quantitative real-time PCR replication of nine lncRNAs was performed in 32 subjects, validating six lncRNAs (AF119885, AK021444, NR_027830, G36810, NR_027927, uc.345-). A coding-non-coding gene co-expression network revealed that four of these six lncRNAs may be correlated with 11 mRNAs and pathway analysis revealed that they may be correlated with another 8 mRNAs associated with metabolic pathways. In conclusion, aberrantly expressed lncRNAs for GSV varicosities were here systematically screened and validated and their functions were predicted. These findings provide novel insight into the physiology of lncRNAs and the pathogenesis of varicose veins for further investigation. These aberrantly expressed lncRNAs may serve as new therapeutic targets for varicose veins. The Human Ethnics Committee of Shanghai East Hospital, Tongji University School of Medicine approved the study (NO.: 2011-DF-53). [ABSTRACT FROM AUTHOR]

Background: Epidemiological surveys and animal studies have revealed that inositol metabolism is associated with NTDs, but the mechanisms are not clear. Inositol 1,3,4-trisphosphate 5/6-kinase (ITPK1) is a pivotal regulatory enzyme in inositol metabolic pathway. The objective was to assess the potential impact of the maternal ITPK1 genotypes on the inositol parameter and on the NTD risk in a NTD high-risk area in China. Methodology/Results: A case-control study of pregnant women affected with NTDs (n = 200) and controls (n = 320) was carried out. 13 tag SNPs of ITPK1 were selected and genotyped by the Sequenom MassArray system. We found that 4 tag SNPs were statistically significant in spina bifida group (P<0.05). MACH was used to impute the un-genotyped SNPs in ITPK1 locus and showed that 3 meaningful SNPs in the non-coding regions were significant. We also predicted the binding capacity of transcription factors in the positive SNPs using the bioinformatics method and found that only rs3783903 was located in the conserved sequence of activator protein-1 (AP-1). To further study the association between biochemical values and genotypes, maternal plasma inositol hexakisphosphate (IP6) levels were also assessed using LC-MS. The maternal plasma IP6 concentrations in the spina bifida subgroup were 7.1% lower than control (136.67 vs. 147.05 ng mL−1, P<0.05), and significantly lower in rs3783903 GG genotype than others (P<0.05). EMSA showed a different allelic binding capacity of AP-1 in rs3783903, which was affected by an A→G exchange. The RT-PCR suggested the ITPK1 expression was decreased significantly in mutant-type of rs3783903 compared with wild-type in the 60 healthy pregnancies (P<0.05). Conclusions/Significance: These results suggested that the maternal rs3783903 of ITPK1 might be associated with spina bifida, and the allele G of rs3783903 might affect the binding of AP-1 and the decrease of maternal plasma IP6 concentration in this Chinese population. [ABSTRACT FROM AUTHOR]

Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ( strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents (DFA) and (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning and . Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true . The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways. [ABSTRACT FROM AUTHOR]

The 1986 accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history, and excess thyroid cancers, particularly among those exposed to releases of iodine-131 remain the best-documented sequelae. Failure to take dose-measurement error into account can lead to bias in assessments of dose-response slope. Although risks in the Ukrainian-US thyroid screening study have been previously evaluated, errors in dose assessments have not been addressed hitherto. Dose-response patterns were examined in a thyroid screening prevalence cohort of 13,127 persons aged <18 at the time of the accident who were resident in the most radioactively contaminated regions of Ukraine. We extended earlier analyses in this cohort by adjusting for dose error in the recently developed TD-10 dosimetry. Three methods of statistical correction, via two types of regression calibration, and Monte Carlo maximum-likelihood, were applied to the doses that can be derived from the ratio of thyroid activity to thyroid mass. The two components that make up this ratio have different types of error, Berkson error for thyroid mass and classical error for thyroid activity. The first regression-calibration method yielded estimates of excess odds ratio of 5.78 Gy−1 (95% CI 1.92, 27.04), about 7% higher than estimates unadjusted for dose error. The second regression-calibration method gave an excess odds ratio of 4.78 Gy−1 (95% CI 1.64, 19.69), about 11% lower than unadjusted analysis. The Monte Carlo maximum-likelihood method produced an excess odds ratio of 4.93 Gy−1 (95% CI 1.67, 19.90), about 8% lower than unadjusted analysis. There are borderline-significant (p = 0.101–0.112) indications of downward curvature in the dose response, allowing for which nearly doubled the low-dose linear coefficient. In conclusion, dose-error adjustment has comparatively modest effects on regression parameters, a consequence of the relatively small errors, of a mixture of Berkson and classical form, associated with thyroid dose assessment. [ABSTRACT FROM AUTHOR]

Visual fields measured with standard automated perimetry are a benchmark test for determining retinal function in ocular pathologies such as glaucoma. Their monitoring over time is crucial in detecting change in disease course and, therefore, in prompting clinical intervention and defining endpoints in clinical trials of new therapies. However, conventional change detection methods do not take into account non-stationary measurement variability or spatial correlation present in these measures. An inferential statistical model, denoted ‘Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement’ (ANSWERS), was proposed. In contrast to commonly used ordinary linear regression models, which assume normally distributed errors, ANSWERS incorporates non-stationary variability modelled as a mixture of Weibull distributions. Spatial correlation of measurements was also included into the model using a Bayesian framework. It was evaluated using a large dataset of visual field measurements acquired from electronic health records, and was compared with other widely used methods for detecting deterioration in retinal function. ANSWERS was able to detect deterioration significantly earlier than conventional methods, at matched false positive rates. Statistical sensitivity in detecting deterioration was also significantly better, especially in short time series. Furthermore, the spatial correlation utilised in ANSWERS was shown to improve the ability to detect deterioration, compared to equivalent models without spatial correlation, especially in short follow-up series. ANSWERS is a new efficient method for detecting changes in retinal function. It allows for better detection of change, more efficient endpoints and can potentially shorten the time in clinical trials for new therapies. [ABSTRACT FROM AUTHOR]

Context: Fetal growth involves highly complex molecular pathways. IGF2 is a key paternally expressed growth hormone that is critical for in utero growth in mice. Its role in human fetal growth has remained ambiguous, as it has only been studied in term tissues. Conversely the maternally expressed growth suppressor, PHLDA2, has a significant negative correlation between its term placental expression and birth weight. Objective: The aim of this study is to address the role in early gestation of expression of IGF1, IGF2, their receptors IGF1R and IGF2R, and PHLDA2 on term birth weight. Design: Real-time quantitative PCR was used to investigate mRNA expression of IGF1, IGF2, IGF1R, IGF2R and PHLDA2 in chorionic villus samples (CVS) (n = 260) collected at 11–13 weeks' gestation. Expression was correlated with term birth weight using statistical package R including correction for several confounding factors. Results: Transcript levels of IGF2 and IGF2R revealed a significant positive correlation with birth weight (0.009 and 0.04, respectively). No effect was observed for IGF1, IGF1R or PHLDA2 and birth weight. Critically, small for gestational age (SGA) neonates had significantly lower IGF2 levels than appropriate for gestational age neonates (p = 3·6×10−7). Interpretation: Our findings show that IGF2 mRNA levels at 12 weeks gestation could provide a useful predictor of future fetal growth to term, potentially predicting SGA babies. SGA babies are known to be at a higher risk for type 2 diabetes. This research reveals an imprinted, parentally driven rheostat for in utero growth. [ABSTRACT FROM AUTHOR]

Vaccination is one of the most efficient ways to control the spread of infectious diseases. Simulations are now widely used to assess how vaccination can limit disease spread as well as mitigate morbidity or mortality in susceptible populations. However, field studies investigating how much vaccines decrease the velocity of epizootic wave-fronts during outbreaks are rare. This study aimed at investigating the effect of vaccination on the propagation of bluetongue, a vector-borne disease of ruminants. We used data from the 2008 bluetongue virus serotype 1 (BTV-1) epizootic of southwest France. As the virus was newly introduced in this area, natural immunity of livestock was absent. This allowed determination of the role of vaccination in changing the velocity of bluetongue spread while accounting for environmental factors that possibly influenced it. The average estimated velocity across the country despite restriction on animal movements was 5.4 km/day, which is very similar to the velocity of spread of the bluetongue virus serotype 8 epizootic in France also estimated in a context of restrictions on animal movements. Vaccination significantly reduced the propagation velocity of BTV-1. In comparison to municipalities with no vaccine coverage, the velocity of BTV-1 spread decreased by 1.7 km/day in municipalities with immunized animals. For the first time, the effect of vaccination has been quantified using data from a real epizootic whilst accounting for environmental factors known to modify the velocity of bluetongue spread. Our findings emphasize the importance of vaccination in limiting disease spread across natural landscape. Finally, environmental factors, specifically those related to vector abundance and activity, were found to be good predictors of the velocity of BTV-1 spread, indicating that these variables need to be adequately accounted for when evaluating the role of vaccination on bluetongue spread. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play a critical role in gastric cancer progression and metastasis. This study investigated the role of miRNA-135a in early gastric cancer (EGC) including lymph node (LN) metastasis. We examined the correlation between miRNA-135a expression and clinical outcomes in 59 patients who underwent surgery for EGC. Using gastric cancer cell lines, we performed functional and target gene analyses. miRNA-135a expression was down-regulated in 33.9% of patients. These patients showed a significantly more advanced stage (TNM stage≥IB, 35.0% vs. 12.8%, p = 0.045) and higher rate of LN metastasis (30.0% vs. 5.1%, p = 0.014) than those with up-regulation of miRNA-135a expression. In a multivariate analysis, down-regulation of miRNA-135a was an independent risk factor for LN metastasis (adjusted odds ratio, 8.04; 95% confidence interval, 1.08–59.81; p = 0.042). Functional analyses using gastric cancer cell lines showed that miRNA-135a suppressed cell viability, epithelial-mesenchymal transition, cell invasion, and migration. ROCK1 was a target of miRNA-135a and its expression was inversely correlated to that of miRNA-135a. ROCK1 expression was significantly increased in EGC patients with LN metastasis than in those without LN metastasis. Our results confirm the tumor-suppressive role of miRNA-135a, and demonstrate its role in LN metastasis in EGC. miRNA-135a and its target gene ROCK1 may be novel therapeutic and prognostic targets for EGC. [ABSTRACT FROM AUTHOR]

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques. [ABSTRACT FROM AUTHOR]

Background: Accelerometers are designed to measure plausible human activity, however extremely high count values (EHCV) have been recorded in large-scale studies. Using population data, we develop methodological principles for establishing an EHCV threshold, propose a threshold to define EHCV in the ActiGraph GT1M, determine occurrences of EHCV in a large-scale study, identify device-specific error values, and investigate the influence of varying EHCV thresholds on daily vigorous PA (VPA). Methods: We estimated quantiles to analyse the distribution of all accelerometer positive count values obtained from 9005 seven-year old children participating in the UK Millennium Cohort Study. A threshold to identify EHCV was derived by differentiating the quantile function. Data were screened for device-specific error count values and EHCV, and a sensitivity analysis conducted to compare daily VPA estimates using three approaches to accounting for EHCV. Results: Using our proposed threshold of ≥ 11,715 counts/minute to identify EHCV, we found that only 0.7% of all non-zero counts measured in MCS children were EHCV; in 99.7% of these children, EHCV comprised < 1% of total non-zero counts. Only 11 MCS children (0.12% of sample) returned accelerometers that contained negative counts; out of 237 such values, 211 counts were equal to −32,768 in one child. The medians of daily minutes spent in VPA obtained without excluding EHCV, and when using a higher threshold (≥19,442 counts/minute) were, respectively, 6.2% and 4.6% higher than when using our threshold (6.5 minutes; p<0.0001). Conclusions: Quality control processes should be undertaken during accelerometer fieldwork and prior to analysing data to identify monitors recording error values and EHCV. The proposed threshold will improve the validity of VPA estimates in children’s studies using the ActiGraph GT1M by ensuring only plausible data are analysed. These methods can be applied to define appropriate EHCV thresholds for different accelerometer models. [ABSTRACT FROM AUTHOR]

Objective: Understanding variations in size and pattern of development of angiotensin II (Ang II)-induced abdominal aortic aneurysms (AAA) may inform translational research strategies. Thus, we sought insight into the temporal evolution of AAA in apolipoprotein (apo)E−/− mice. Approach: A cohort of mice underwent a 4-week pump-mediated infusion of saline (n = 23) or 1500 ng/kg/min of Ang II (n = 85) and AAA development was tracked via in vivo ultrasound imaging. We adjusted for hemodynamic covariates in the regression models for AAA occurrence in relation to time. Results: The overall effect of time was statistically significant (p<0.001). Compared to day 7 of AngII infusion, there was no decrease in the log odds of AAA occurrence by day 14 (−0.234, p = 0.65), but compared to day 21 and 28, the log odds decreased by 9.07 (p<0.001) and 2.35 (p = 0.04), respectively. Hemodynamic parameters were not predictive of change in aortic diameter (Δ) (SBP, p = 0.66; DBP, p = 0.66). Mean total cholesterol (TC) was higher among mice with large versus small AAA (601 vs. 422 mg/ml, p<0.0001), and the difference was due to LDL. AngII exposure was associated with 0.43 mm (95% CI, 0.27 to 0.61, p<0.0001) increase in aortic diameter; and a 100 mg/dl increase in mean final cholesterol level was associated with a 12% (95% CI, 5.68 to 18.23, p<0.0001) increase in aortic diameter. Baseline cholesterol was not associated with change in aortic diameter (p = 0.86). Conclusions: These are the first formal estimates of a consistent pattern of Ang II-induced AAA development. The odds of AAA occurrence diminish after the second week of Ang II infusion, and TC is independently associated with AAA size. [ABSTRACT FROM AUTHOR]

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discriminatory power of a prediction rule. Specifically, we propose a gradient boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures. [ABSTRACT FROM AUTHOR]