Renal failure in experimental sepsis: role of Endothelin and the Toll-like receptor 4

Sepsis is the leading cause of renal failure in critically ill patients, but the pathogenesis of septic kidney dysfunction is poorly defined. The current paradigm states that hypoperfusion and excessive renal vasoconstriction results in renal ischemia. However, experimental data also exist indicating a direct immune-mediated basis of septic renal impairment. This thesis aimed to investigate the contribution of both a potent vasoconstrictor peptide, endothelin-1 (ET-1), and a receptor that activates the immune system in response to a bacterial infection, the Toll-like receptor 4 (TLR4), to the renal failure caused by sepsis. The first part of this thesis investigated the role of the endothelin system in renal microcirculatory and functional impairment caused by experimental septic renal failure, by studying the effects of dual endothelin type A and B (ETA/ETB) antagonism and selective ETA-antagonism during porcine endotoxemia. ET-1 is a vasoconstrictor peptide that is a potent modulator of microcirculatory blood flow. It is released in high amounts during sepsis and experimental data have shown that ET-1 reduces renal blood flow. In paper I and II pigs were subjected to lipopolysaccharide (LPS) infusion and the renal microcirculatory effects of dual ETA/ETB or selective ETA antagonism were investigated. The main findings were that dual ETA/ETB blockade attenuated the endotoxemia induced reduction in renal cortical microcirculation, as well as the increase in plasma creatinine levels (paper I). In addition, selective ETA antagonism reduced the decline in renal medullary microcirculation, but had no significant effect on diuresis or creatinine clearance (paper II). The second part of this thesis investigated the role of TLR4 activation in renal failure caused by hyperdynamic endotoxemia or sepsis. Conscious surgically prepared sheep were subjected to LPS or live Escherichia coli infusion and observed for 24-36 hours. A main finding was that pretreatment with a TLR4-inhibitor attenuated renal failure and hypotension caused by endotoxemia in sheep. This effect was greater compared to norepinephrine treatment, in a dose that prevented hypotension (paper III). Moreover, it was observed that septic renal failure developed without renal hypoperfusion and that treatment with a TLR4-inhibitor reversed renal failure when administered 12 hours into sepsis (paper IV). This effect was independent of changes in systemic or renal hemodynamics but was associated with a reduced renal neutrophil accumulation. In conclusion, despite no reduction in renal perfusion or arterial blood pressure, septic renal failure may still develop. During hyperdynamic sepsis, stimulation of the innate immune system, via TLR4 activation, may contribute to the development of renal failure. In addition, TLR4-inhibition is an effective treatment to improve renal function in ovine sepsis induced by E.coli. In hypodynamic endotoxemia, ET-1 contributes to renal vasoconstriction. By acting on ETA, ET-1 reduces renal medullary blood flow causing ischemia but has no short-term effect on renal function

Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.</p

Use of Machine Learning for Automated Convergence of Numerical Iterative Schemes

Convergence of a numerical solution scheme occurs when a sequence of increasingly refined iterative solutions approaches a value consistent with the modeled phenomenon. Approximations using iterative schemes need to satisfy convergence criteria, such as reaching a specific error tolerance or number of iterations. The schemes often bypass the criteria or prematurely converge because of oscillations that may be inherent to the solution. Using a Support Vector Machines (SVM) machine learning approach, an algorithm is designed to use the source data to train a model to predict convergence in the solution process and stop unnecessary iterations. The discretization of the Navier Stokes (NS) equations for a transient local hemodynamics case requires determining a pressure correction term from a Poisson-like equation at every time-step. The pressure correction solution must fully converge to avoid introducing a mass imbalance. Considering time, frequency, and time-frequency domain features of its residual’s behavior, the algorithm trains an SVM model to predict the convergence of the Poisson equation iterative solver so that the time-marching process can move forward efficiently and effectively. The fluid flow model integrates peripheral circulation using a lumped-parameter model (LPM) to capture the field pressures and flows across various circulatory compartments. Machine learning opens the doors to an intelligent approach for iterative solutions by replacing prescribed criteria with an algorithm that uses the data set itself to predict convergence

On the Indeterminates of Glaucoma:the Controversy of Arterial Blood Pressure and Retinal Perfusion

Glaucoma is a chronic eye disease characterized by thinning of the retina, death of ganglion cells, and progressive loss of vision, eventually leading to blindness. The prevalence of glaucoma is estimated at 1-3% of those over 40 years old. With a constantly aging population, this number is expected to increase significantly over the next 10 years. Even with treatment, about 15% of people with glaucoma currently develop residual vision or tunnel vision and eventually become blind or partially sighted. The mechanisms behind ganglion cell death are poorly understood. Elevated eye pressure is the main risk factor for glaucoma, but treatment in the form of medication, laser, or surgery can only slow the decline, not stop it. In addition, high intraocular pressure is neither necessary nor sufficient for the development of glaucoma, indicating the existence of other unknown risk factors. It has been established that the death of ganglion cells results in a decreased oxygen demand and a concomitant decrease in blood flow. However, there is also a hypothesis that reduced or unstable blood supply is not only a consequence, but also a cause of glaucoma. This is known as the ‘chicken-egg’ dilemma in glaucoma. It is supported by the observation that the risk of developing glaucoma is higher in people with very low blood pressure (sometimes even as a result of overtreatment of high blood pressure).This dissertation is an attempt to methodically examine whether blood pressure can be linked to changes in the retina that could suggest susceptibility to glaucoma. For this purpose, we analyze epidemiological data from the Groningen Longitudinal Glaucoma Study, we use advanced imaging techniques to model the microcirculation, and we describe its relationship with the neural structure and oxygen consumption of the retina. We provide evidence leaning towards the existence of a vascular component, likely pertinent to glaucoma

Combining the Performance Strengths of the Logistic Regression and Neural Network Models: A Medical Outcomes Approach

The assessment of medical outcomes is important in the effort to contain costs, streamline patient management, and codify medical practices. As such, it is necessary to develop predictive models that will make accurate predictions of these outcomes. The neural network methodology has often been shown to perform as well, if not better, than the logistic regression methodology in terms of sample predictive performance. However, the logistic regression method is capable of providing an explanation regarding the relationship(s) between variables. This explanation is often crucial to understanding the clinical underpinnings of the disease process. Given the respective strengths of the methodologies in question, the combined use of a statistical (i.e., logistic regression) and machine learning (i.e., neural network) technology in the classification of medical outcomes is warranted under appropriate conditions. The study discusses these conditions and describes an approach for combining the strengths of the models