Sleep and body rhythm disturbance amongst flight crews in long-range aviation

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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).

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.

An investigation into the effects of metabolic disturbance on monocyte inflammatory response and regulation

The presence of chronic inflammation is associated with increased nutrient availability during obesity or type 2 diabetes which contributes to the development of complications such as atherosclerosis, stroke and myocardial infarction. The link between increased nutrient availability and inflammatory response remains poorly understood. The functioning of monocytes, the primary instigators of the inflammatory response was assessed in response to obesity and increased glucose availability. Monocyte microRNA expression was assessed in obese individuals prior to and up to one year after bariatric surgery. A number of microRNAs were identified to be dysregulated in obesity, some of which have previously been linked to the regulation of monocyte inflammatory responses including the microRNAs 146a-5p and 424-5p. Weight loss in response to bariatric surgery lead to the reversal of microRNA changes towards control values. In vitro treatments of THP-1 monocytes with high concentrations of D-glucose resulted in decreased intracellular NAD+:NADH ratio, decreased SIRT1 deacetylase activity and increased P65 acetylation. However the increased osmotic concentration inhibited LPS induced inflammatory response and TNFα mRNA expression. In vitro treatment of primary human monocytes with increased concentrations of D-glucose resulted in increased secretion of a number of inflammatory cytokines and increased expression of TNFα mRNA. Treatment also resulted in decreased intracellular NAD+:NADH ratio and increased binding of acetylated P65 to the TNFα promoter region. In vitro treatments of primary monocytes also replicated the altered expression of the microRNAs 146a-5p and miR-424-5p, as seen in obese individuals. In conclusion a number of changes in monocyte function were observed in response to obesity and treatment with high concentrations of D-glucose. These may lead to the dysregulation of inflammatory responses contributing to the development of co-morbidities.

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.

Mechatronic solutions for measuring disturbance levels within a cochlea

Hearing implants are an important devices for combating deafness over the next 15 years. In this paper, we focus on the means to determine the sensitivity of the hearing organ to disturbances produced by implants and other interventions, and those induced by implantation. The preservation of residual hearing is an important aspect to be considered, however, the sensitivity of this to the process of implantation, device location and power levels is not well understood. Within this paper, a new experimental set-up to contrast the merits of different implantation techniques, implant location and power transmission are discussed and the initial results regarding disturbance levels using different surgical techniques are described.