Proteomics in aortic aneurysm – What have we learnt so far?

Aortic aneurysm is a deceptively indolent disease that can cause severe complications such as aortic rupture and dissection. In the normal aorta, vascular smooth muscle cells within the medial layer produce and sustain the extracellular matrix (ECM) that provides structural support but also retains soluble growth factors and regulates their distribution. Although the ECM is an obvious target to identify molecular processes leading to structural failure within the vessel wall, an in-depth proteomics analysis of this important sub-proteome has not been performed. Most proteomics analyses of the vasculature to date used homogenized tissue devoid of spatial information. In such homogenates, quantitative proteomics comparisons are hampered by the heterogeneity of clinical samples (i.e. cellular composition) and the dynamic range limitations stemming from highly abundant cellular proteins. An unbiased proteomics discovery approach targeting the ECM instead of the cellular proteome may decipher the complex, multivalent signals that are presented to cells during aortic remodelling. A better understanding of the ECM in healthy and diseased vessels will provide important pathogenic insights and has potential to reveal novel biomarkers.     

Modeling the rate of corrosion of carbon steel using activated diethanolamine solutions for CO2 absorption

A mechanistic model is developed to investigate the influence of an activator on the corrosion rate of carbon steel in the absorption processes of carbon dioxide (CO₂). Piperazine (PZ) is used as the activator in diethanolamine (DEA) aqueous solutions. The developed model for corrosion takes into consideration the effect of fluid flow, transfer of charge and diffusion of oxidizing agents and operating parameters like temperature, activator concentration, CO₂ loading and pH. The study consists of two major models: Vapor-liquid Equilibrium (VLE) model and electrochemical corrosion model. The electrolyte-NRTL equilibrium model was used for determination of concentration of chemical species in the bulk solution. The results of speciation were subsequently used for producing polarization curves and predicting the rate of corrosion occurring at the surface of metal. An increase in concentration of activator, increases the rate of corrosion of carbon steel in mixtures of activated DEA.     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Interval analysis of mode shapes to identify damage in beam structures

Various damage detection methods have been proposed by several researchers in the past few decades. Amongst them, the efficiency of mode shapes in detecting damage has been demonstrated by many researchers when further processed. In most cases, the processing involves expansion or reduction of the mode shape data. However, vital information that are damage-prints are often lost during processing of the mode shape data. In addition, most of these processes involve long and complex computation, thus, leading to inaccurate damage identification. In this study, a simple and fast damage identification technique is proposed to identify damage in beam structures. Interval analysis is applied to the mode shapes of a beam structure in the damaged and undamaged states. The interval situations of each of the beam's segment via mode shape are derived to obtain the upper and lower bounds and the derived bounds are compared. To establish a relationship for identify the damaged point, a possibility of damage existence is defined for each segment of the beam structure. The mode shape increment is defined as the increase in the mode shape value. Furthermore, a damage measure index that provide enhance damage information is obtained as the product of the possibility of damage existence and mode shape increment. A numerical model of a simply supported steel beam is applied to demonstrate this method by imposing damage through thickness reduction of elements in segments. In addition, a parametric analysis is carried out to evaluate noise effect by considering varying damage severities and different noise levels. The results showed that this method is simple and provides considerable accurate results.     

Estimation of colloidal deposition from heterogeneous populations

This paper reports a study of size-heterogeneous colloid filtration in a new bed using different types of colloids under different conditions of flow and solution chemistry. Depth-wise variation of the particle-size-distributions f_i(x), and the total liquid-phase colloid concentration, C(x) are measured which are used to estimate the depth-wise variation of the liquid-phase concentration for each distinct section of the heterogeneous population, C_i(x). It is observed that log C_i(x) is linear with depth, for some systems, while it shows deviation from linearity, with the slope decreasing with depth, for others. Deposition-rates for these distinct sections of the heterogeneous population, k_i, are estimated from the slopes of the log C_i(x) data. These deposition-rates were then compared with predicted homogeneous-population deposition-rates from Colloid Filtration Theory (CFT), which shows agreement between the CFT-based-deposition-rates and heterogeneous-population-data based deposition-rates, for low flow velocities. At higher flow velocities a gap between the CFT-based and Data-based deposition-rates is observed. Deposition-rates from CFT are then used in a heterogeneous-colloid-filtration model, to examine if heterogeneous colloid deposition can be expressed as the sum of its parts. It is observed that, the sum-of-parts model provides a reasonable estimate of colloidal deposition from heterogeneous populations. Based on these results, it is possible to make predictions of colloidal deposition from complex heterogeneous suspensions. A new method for studying heterogeneous colloid filtration is also proposed.     

Changes in biochemical,hemodynamic, and dialysis adherence parameters in hemodialysis patients during Ramadan

This paper aimed to study the effect of Ramadan fasting on biochemical and clinical parameters and compliance for dialysis. A prospective multicenter observational cross‐sectional study comparing fasting with a non‐fasting stable adult hemodialysis patients for demographic and biochemical parameters, compliance with dialysis, inter‐dialytic weight gain, pre‐ and post‐blood pressure, and frequency of intradialytic hypotensive episodes was carried out. Six hundred thirty‐five patients, of whom 64.1% fasted, were studied. The fasters were younger (53.3 ± 16.2 vs. 58.4 ± 16.1 years; P = 0.001) but had similar duration on dialysis (P = 0.35). More fasters worked (22.0% vs. 14.6%; P = 0.001) and missed dialysis sessions during Ramadan. No differences were noted between groups in sex, diabetic status, or dialysis shift or day. There were no differences in the pre‐ and post‐dialysis blood pressure; serum potassium, albumin or weight gain; diabetic status; sex; and dialysis shift time or days. However, serum phosphorous was significantly higher in the fasting group (2.78 ± 1.8 vs. 2.45 ± 1.6 mmol/L; P = 0.045). There were no intragroup differences in any of the parameters studied when comparing the findings during Ramadan with those in the month before Ramadan. Fasters were significantly younger and more likely to be working, to miss dialysis sessions, and to have higher serum phosphorous levels. No other differences were observed.     

PCF-multimode/endless fiber sensor for respiratory rate monitoring

A respiratory monitoring system was proposed based on optical fiber. The sensor consists of photonic crystal fiber (PCF) spliced with multimode fiber (MMF) to fabricate Michelson interferometer. The sensor is inserted into an oxygen mask tube and then put on the nose and mouth to monitor the respiratory rate (RR) for different cases. The results showed that when breathing increased, the intensity of light decreased. The intensity of light in the sleeping case of breathing was more intense than that when the intensity of working sport was measured. The working sport case was a higher transmittance than that of sleeping. The sensitivity of the sensor was also measured to be 231.9 pm/RH.     

Predictive mathematical modeling and computer simulation of direct ethanol fuel cell

The challenges of finding a better substitute of energy as well as the shortcomings identified with direct ethanol fuel cell, includes high anode over potential and crossover necessitate the need to investigate the influence operating parameters on the performance of fuel through computer simulation. This study focus on the development of a predictive mathematical modeling for direct ethanol fuel cell for the purpose of investigating the influence pressure, temperature cathode, and reactants concentration on the performance, efficiency, and heat generated by the cell. Results obtained indicate that an increase in operating temperature led to a decrement in output voltage and cell efficiency, while the same condition of increasing the temperature positively favors the heat generated from the cell. Simulated results also show that cell performance is improved with an increase in concentration of the fuel (ethanol) and oxidant (oxygen). It can be inferred from this study that the cell performance of DEFC can be theoretically predicted with the developed model.