The effect of cis-5,8,11,14,17-eicosapentaenoic acid upon the contractile mechanisms linked to calcium influx and the mobilisation of intracellular calcium in aortic smooth muscle

Epidemiological studies previously identified cis-5,8,11,14,17-eicosapentaenoic acid (EPA) as the biologically active component of fish oil of benefit to the cardiovascular system. Although clinical investigations demonstrated its usefulness in surgical procedures, its mechanism of action still remained unclear. It was shown in this thesis, that EPA partially blocked the contraction of aortic smooth muscle cells to the vasoactive agents KCl and noradrenaline. The latter effect was likely caused by reducing calcium influx through receptor-operated channels, supporting a recent suggestion by Asano et al (1997). Consistently, EPA decreased noradrenaline-induced contractures in aortic tissue, in support of previous reports (Engler, 1992b). The observed effect of EPA on cell contractions to KCl was not simple due to blocking calcium influx through L-type channels, consistent with a previous suggestion by Hallaq et al (1992). Moreover, EPA caused a transient increase in [Ca2+]i in the absence of extracellular calcium. To resolve this it was shown that EPA increased inositol phosphate formation which, it is suggested, caused the release of calcium from an inositol phosphate-dependent internal binding site, possibly that of an intracellular membrane or superficial sarcoplasmic reticulum, producing the transient increase in [Ca2+]i. As it was shown that the cellular contractile filaments were not desensitised to calcium by EPA, it is suggested that the transient increase in [Ca2+]i subsequently blocks further cell contraction to KCl by activating membrane-associated potassium channels. Activation of potassium channels induces the cellular efflux of potassium ions, thereby hyperpolarising the plasma membrane and moving the membrane potential farther from the activation range for calcium channels. This would prevent calcium influx in the longer term and could explain the initial observed effect of EPA to block cell contraction to KCl.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

Handling high level of censoring for endovascular aortic repair risk prediction

Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.

The risk of re-intervention after endovascular aortic aneurysm repair

This thesis studies survival analysis techniques dealing with censoring to produce predictive tools that predict the risk of endovascular aortic aneurysm repair (EVAR) re-intervention. Censoring indicates that some patients do not continue follow up, so their outcome class is unknown. Methods dealing with censoring have drawbacks and cannot handle the high censoring of the two EVAR datasets collected. Therefore, this thesis presents a new solution to high censoring by modifying an approach that was incapable of differentiating between risks groups of aortic complications. Feature selection (FS) becomes complicated with censoring. Most survival FS methods depends on Cox's model, however machine learning classifiers (MLC) are preferred. Few methods adopted MLC to perform survival FS, but they cannot be used with high censoring. This thesis proposes two FS methods which use MLC to evaluate features. The two FS methods use the new solution to deal with censoring. They combine factor analysis with greedy stepwise FS search which allows eliminated features to enter the FS process. The first FS method searches for the best neural networks' configuration and subset of features. The second approach combines support vector machines, neural networks, and K nearest neighbor classifiers using simple and weighted majority voting to construct a multiple classifier system (MCS) for improving the performance of individual classifiers. It presents a new hybrid FS process by using MCS as a wrapper method and merging it with the iterated feature ranking filter method to further reduce the features. The proposed techniques outperformed FS methods based on Cox's model such as; Akaike and Bayesian information criteria, and least absolute shrinkage and selector operator in the log-rank test's p-values, sensitivity, and concordance. This proves that the proposed techniques are more powerful in correctly predicting the risk of re-intervention. Consequently, they enable doctors to set patients’ appropriate future observation plan.