The mechanical triggers of plaque rupture: Shear stress vs pressure gradient

The aim of this study was to evaluate the mechanical triggers that may cause plaque rupture. Wall shear stress (WSS) and pressure gradient are the direct mechanical forces acting on the plaque in a stenotic artery. Their influence on plaque stability is thought to be controversial. This study used a physiologically realistic, pulsatile flow, two-dimensional, cine phase-contrast MRI sequence in a patient with a 70% carotid stenosis. Instead of considering the full patient-specific carotid bifurcation derived from MRI, only the plaque region has been modelled by means of the idealised flow model. WSS reached a local maximum just distal to the stenosis followed by a negative local minimum. A pressure drop across the stenosis was found which varied significantly during systole and diastole. The ratio of the relative importance of WSS and pressure was assessed and was found to be less than 0.07% for all time phases, even at the throat of the stenosis. In conclusion, although the local high WSS at the stenosis may damage the endothelium and fissure plaque, the magnitude of WSS is small compared with the overall loading on plaque. Therefore, pressure may be the main mechanical trigger for plaque rupture and risk stratification using stress analysis of plaque stability may only need to consider the pressure effect.

The impact of wall shear stress and pressure drop on the stability of the atherosclerotic plaque

Rupture of vulnerable atheromatous plaque in the carotid and coronary arteries often leads to stroke and heart attack respectively. The mechanism of blood flow and plaque rupture in stenotic arteries is still not fully understood. A three dimensional rigid wall model was solved under steady state conditions and unsteady conditions by assuming a time-varying inlet velocity profile to investigate the relative importance of axial forces and pressure drops in arteries with asymmetric stenosis. Flow-structure interactions were investigated for the same geometry and the results were compared with those retrieved with the corresponding 2D cross-section structural models. The Navier-Stokes equations were used as the governing equations for the fluid. The tube wall was assumed hyperelastic, homogeneous, isotropic and incompressible. The analysis showed that the three dimensional behavior of velocity, pressure and wall shear stress is in general very different from that predicted by cross-section models. Pressure drop across the stenosis was found to be much higher than shear stress. Therefore, pressure may be the more important mechanical trigger for plaque rupture other than shear stress, although shear stress is closely related to plaque formation and progression.

Simulation of the interaction between blood flow and atherosclerotic plaque

It has been well accepted that over 50% of cerebral ischemic events are the result of rupture of vulnerable carotid atheroma and subsequent thrombosis. Such strokes are potentially preventable by carotid interventions. Selection of patients for intervention is currently based on the severity of carotid luminal stenosis. It has been, however, widely accepted that luminal stenosis alone may not be an adequate predictor of risk. To evaluate the effects of degree of luminal stenosis and plaque morphology on plaque stability, we used a coupled nonlinear time-dependent model with flow-plaque interaction simulation to perform flow and stress/strain analysis for stenotic artery with a plaque. The Navier-Stokes equations in the Arbitrary Lagrangian-Eulerian (ALE) formulation were used as the governing equations for the fluid. The Ogden strain energy function was used for both the fibrous cap and the lipid pool. The plaque Principal stresses and flow conditions were calculated for every case when varying the fibrous cap thickness from 0.1 to 2mm and the degree of luminal stenosis from 10% to 90%. Severe stenosis led to high flow velocities and high shear stresses, but a low or even negative pressure at the throat of the stenosis. Higher degree of stenosis and thinner fibrous cap led to larger plaque stresses, and a 50% decrease of fibrous cap thickness resulted in a 200% increase of maximum stress. This model suggests that fibrous cap thickness is critically related to plaque vulnerability and that, even within presence of moderate stenosis, may play an important role in the future risk stratification of those patients when identified in vivo using high resolution MR imaging.

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.

Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests

Selection criteria and misspecification tests for the intra-cluster correlation structure (ICS) in longitudinal data analysis are considered. In particular, the asymptotical distribution of the correlation information criterion (CIC) is derived and a new method for selecting a working ICS is proposed by standardizing the selection criterion as the p-value. The CIC test is found to be powerful in detecting misspecification of the working ICS structures, while with respect to the working ICS selection, the standardized CIC test is also shown to have satisfactory performance. Some simulation studies and applications to two real longitudinal datasets are made to illustrate how these criteria and tests might be useful.

Working covariance model selection for generalized estimating equations

We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice.

Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.

Estimation for the general sample selection models

Consider a general regression model with an arbitrary and unknown link function and a stochastic selection variable that determines whether the outcome variable is observable or missing. The paper proposes U-statistics that are based on kernel functions as estimators for the directions of the parameter vectors in the link function and the selection equation, and shows that these estimators are consistent and asymptotically normal.

Criteria for working-correlation-structure selection in GEE: Assessment via simulation

Efficiency of analysis using generalized estimation equations is enhanced when intracluster correlation structure is accurately modeled. We compare two existing criteria (a quasi-likelihood information criterion, and the Rotnitzky-Jewell criterion) to identify the true correlation structure via simulations with Gaussian or binomial response, covariates varying at cluster or observation level, and exchangeable or AR(l) intracluster correlation structure. Rotnitzky and Jewell's approach performs better when the true intracluster correlation structure is exchangeable, while the quasi-likelihood criteria performs better for an AR(l) structure.

Laboratory investigation on the effects of overburden pressure, water, and time on slaking induced material property degradation of coal mine spoil

In open-cut strip mining, waste material is placed in-pit to minimise operational mine costs. Slope failures in these spoil piles pose a significant safety risk to personnel, along with a financial risk from loss of equipment and scheduling delays. It has been observed that most spoil pile failures occur when the pit has been previously filled with water and then subsequently dewatered. The failures are often initiated at the base of spoil piles where the material can undergo significant slaking (disintegration) over time due to overburden pressure and water saturation. It is important to understand how the mechanical properties of base spoil material are affected by slaking when designing safe spoil pile slope angles, heights, and dewatering rates. In this study, fresh spoil material collected from a coal mine in Brown Basin Coalfield of Queensland, Australia was subjected to high overburden pressure (0 – 900 kPa) under saturated condition and maintained over a period of time (0 – 6 months) allowing the material to slake. To create the above conditions, laboratory designed pressure chambers were used. Once a spoil sample was slaked under certain overburden pressure over a period of time, it was tested for classification, permeability, and strength properties. Results of this testing program suggested that the slaking of saturated coal mine spoil increase with overburden pressure and the time duration over which the overburden pressure was maintained. Further, it was observed that shear strength and permeability of spoil decreased with increase in spoil slaking.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Intracellular effects of atmospheric-pressure plasmas on melanoma cancer cells

Gas discharge plasmas formed at atmospheric pressure and near room temperature have recently been shown as a promising tool for cancer treatment. The mechanism of the plasma action is attributed to generation of reactive oxygen and nitrogen species, electric fields, charges, and photons. The relative importance of different modes of action of atmospheric-pressure plasmas depends on the process parameters and specific treatment objects. Hence, an in-depth understanding of biological mechanisms that underpin plasma-induced death in cancer cells is required to optimise plasma processing conditions. Here, the intracellular factors involved in the observed anti-cancer activity in melanoma Mel007 cells are studied, focusing on the effect of the plasma treatment dose on the expression of tumour suppressor protein TP73. Over-expression of TP73 causes cell growth arrest and/or apoptosis, and hence can potentially be targeted to enhance killing efficacy and selectivity of the plasma treatment. It is shown that the plasma treatment induces dose-dependent up-regulation of TP73 gene expression, resulting in significantly elevated levels of TP73 RNA and protein in plasma-treated melanoma cells. Silencing of TP73 expression by means of RNA interference inhibited the anticancer effects of the plasma, similar to the effect of caspase inhibitor z-VAD or ROS scavenger N-acetyl cysteine. These results confirm the role of TP73 protein in dose-dependent regulation of anticancer activity of atmospheric-pressure plasmas.

Pro-apoptotic NOXA is implicated in atmospheric-pressure plasma-induced melanoma cell death

Atmospheric-pressure plasma (APP) has been successfully used to treat several types of cancers in vivo and in vitro, with the effect being primarily attributed to the generation of reactive oxygen species (ROS). However, the mechanisms by which APP induces apoptosis in cancer cells require further elucidation. In this study, the effects of APP on the expression of 500 genes in melanoma Mel007 cancer cells were examined. Pro-apoptotic phorbol-12-myristate-13-acetate-induced protein (PMAIP1), also known as NOXA, was highly expressed as a result of APP treatment in a dose-dependent manner. Blocking of ROS using scavenger NAC or silencing of NOXA gene by RNA interference inhibited the APP-induced NOXA genes upregulation and impaired caspases 3/7 mediated apoptosis, confirming the important role plasma-generated ROS species and pro-apoptotic NOXA play in APP-induced cancer cell death.

Marker selection by Akaike information criterion and Bayesian information criterion

We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected-affected versus affected-unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Such model comparisons are best carried out using either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). When AIC (or BIC) stepwise variable selection was applied to the German Asthma data set, a group of markers were selected which provide the best fit to the data (assuming an additive effect). Interestingly, these 25-26 markers were not identical to those with the highest (in magnitude) single-locus lod scores.