NT-ProBNP levels in saliva and its clinical relevance to heart failure

Background: Current blood based diagnostic assays to detect heart failure (HF) have large intra-individual and inter-individual variations which have made it difficult to determine whether the changes in the analyte levels reflect an actual change in disease activity. Human saliva mirrors the body's health and well being and similar to 20% of proteins that are present in blood are also found in saliva. Saliva has numerous advantages over blood as a diagnostic fluid which allows for a non-invasive, simple, and safe sample collection. The aim of our study was to develop an immunoassay to detect NT-proBNP in saliva and to determine if there is a correlation with blood levels. Methods: Saliva samples were collected from healthy volunteers (n = 40) who had no underlying heart conditions and HF patients (n = 45) at rest. Samples were stored at -80 degrees C until analysis. A customised homogeneous sandwich AlphaLISA((R)) immunoassay was used to quantify NT-proBNP levels in saliva. Results: Our NT-proBNP immunoassay was validated against a commercial Roche assay on plasma samples collected from HF patients (n = 37) and the correlation was r(2) = 0.78 (p<0.01, y = 1.705 x +1910.8). The median salivary NT-proBNP levels in the healthy and HF participants were <16 pg/mL and 76.8 pg/mL, respectively. The salivary NT-proBNP immunoassay showed a clinical sensitivity of 82.2% and specificity of 100%, positive predictive value of 100% and negative predictive value of 83.3%, with an overall diagnostic accuracy of 90.6%. Conclusion: We have firstly demonstrated that NT-proBNP can be detected in saliva and that the levels were higher in heart failure patients compared with healthy control subjects. Further studies will be needed to demonstrate the clinical relevance of salivary NT-proBNP in unselected, previously undiagnosed populations.

The use of prescribed drugs for common chronic conditions in South Africa in 1998

Purpose To determine the prescribed drug-utilisation pattern for six common chronic conditions in adult South Africans in a cross-sectional survey. Methods 13 826 randomly selected participants, 15 years and older, were surveyed by trained fieldworkers at their homes in 1998. Questionnaires included socio-demographic, chronic-disease and drug-use data. The prescribed drugs were recorded from participants' medication containers. The Anatomical Therapeutic Classification (ATC) code of the drugs for tuberculosis (TB), diabetes, hypertension, hyperlipidaemia, other atherosclerosis-related conditions, such as heart conditions or cerebrovascular accidents (CVA), and asthma or chronic obstructive pulmonary disease (COPD), was recorded. The use of logistic regression analyses identified the determinants of those patients who used prescription medication for these six conditions. Results 18.4% of the women and 12.5% of the men used drugs for the six chronic conditions. Men used drugs most frequently for hypertension (50.9%) and asthma or chronic bronchitis (24.3%), while in women it was for hypertension (59.9%) and diabetes (17.5%). The logistic regression analyses showed that women, wealthier and older people, and those with medical insurance used these chronic-disease drugs more frequently compared to men, younger or poor people, or those without medical insurance. The African population group used these drugs less frequently than any other ethnic group. The inappropriate use of methyldopa was found for 14.8% of all antihypertensive drugs, while very few people used aspirin. Conclusions The methodology of this study provides a means of ascertaining the chronic-disease drug-utilisation pattern in national health surveys. The pattern described, suggests an inequitable use of chronic-disease drugs and inadequate use of some effective drugs to control the burden of chronic diseases in South Africa. Copyright © 2004 John Wiley & Sons, Ltd.

Molecular and structural requirements of the ß1L-adrenoceptor

Noradrenaline which occurs naturally in the body binds to beta-adrenoceptors on the heart, causing the heart to beat faster and with greater force in response to increased demand. This enables the heart to provide oxygenated blood to vital organs. Prolonged overstimulation by noradrenaline can be harmful to the heart and lead to the progression of heart disease. In these circumstances beta-adrenoceptors are blocked with drugs called beta-blockers. Beta-blockers block the effects of noradrenaline by binding to the same site on the beta-adrenoceptor. Some beta-blockers such as CGP12177 can also cause increases in heart rate. Therefore it was proposed that CGP12177 could bind in a different place to noradrenaline. The aim of this study was to determine where CGP12177 binds to on the beta-adrenoceptor. The results have revealed a separate binding site named beta-1-low. These results may lead to the development of improved -blockers for the management of heart conditions.

Validation of an immunoassay to measure plasminogen-activator inhibitor-1 concentrations in human saliva

Introduction We have previously shown that the concentrations of D-dimer are significantly elevated in saliva compared with plasma. Saliva offers several advantages compared with blood analysis. We hypothesised that human saliva contains plasminogen activator inhibitor-1 (PAI-1) and that the concentrations are not affected by the time of saliva collection. The aim was to adopt and validate an immunoassay to quantify PAI-1 concentrations in saliva and to determine whether saliva collection time has an influence in the measurement. Materials and methods Two saliva samples (morning and afternoon) from the same day were collected from healthy subjects (N = 40) who have had no underlying heart conditions. A customized AlphaLISA® immunoassay (PerkinElmer®, MA, USA) was adopted and used to quantify PAI-1 concentrations. We validated the analytical performance of the customized immunoassay by calculating recovery of known amount of analyte spiked in saliva. Results: The recovery (95.03%), intra- (8.59%) and inter-assay (7.52%) variations were within the acceptable ranges. The median salivary PAI-1 concentrations were 394 pg/mL (interquartile ranges (IQR) 243.4-833.1 pg/mL) in the morning and 376 (129.1-615.4) pg/mL in the afternoon and the plasma concentration was 59,000 (24,000-110,000) pg/mL. Salivary PAI-1 did not correlate with plasma (P = 0.812). Conclusions The adopted immunoassay produced acceptable assay sensitivity and specificity. The data demonstrated that saliva contains PAI-1 and that its concentration is not affected by the time of saliva collection. There is no correlation between salivary and plasma PAI-1 concentrations. Further studies are required to demonstrate the utility of salivary PAI-1 in CVD risk factor studies.

Mechanism design of ventricular assist device

This research presents an innovative design approach for the development of high efficiency Ventricular assist device that can be used for long-term support a heart failure patient. Computational fluid dynamics (CFD) techniques were applied to the development and intensive analysis to improve the performance and reliability of the pump. From the CFD analysis, a prototype pump was created and evaluated on the mock circulation loop that simulate the human circulatory system environment to evaluate its performance in support varying heart conditions.

Stem cell Researches in Brazil: Present and Future Challenges

A bill allowing researches with human embryonic stem cells has been approved by the Brazilian Congress, originally in 2005 and definitively by the Supreme Court in 2008. However, several years before, investigations in Brazil with adult stem cells in vitro in animal models as well as clinical trials, were started and are currently underway. Here, we will summarize the main findings and the challenges of going from bench to bed, focusing on heart, diabetes, cancer, craniofacial, and neuromuscular disorders. We also call attention to the importance of publishing negative results on experimental trials in scientific journals and websites. They are of great value to investigators in the field and may avoid the repeating of unsuccessful experiments. In addition, they could be referred to patients seeking information, aiming to protect them against financial and psychological harm.

Effective and efficient detection of premature ventricular contractions based on variation of principal directions

Classification of electrocardiogram (ECG) data stream is essential to diagnosis of critical heart conditions. It is vital to accurately detect abnormality in the ECG in order to prevent possible beginning of life-threatening cardiac symptoms. In this paper, we focus on identifying premature ventricular contraction (PVC) which is one of the most common heart rhythm abnormalities. We use "Replacing" strategy to check the effects of each individual heartbeat on the variation of principal directions. Based on this idea, an online PVC detection method is proposed to classify the new arriving PVC beats in the real-time and online manner. The proposed approach is tested on the MIT-BIH arrhythmia database (MIT-BIH-AR). The PVC detection accuracy was 98.77%, with the sensitivity and positive predictivity of 96.12% and 86.48%, respectively. These results are an improvement on previous reported results for PVC detection. In addition, our proposed method is effective in terms of computation time. The average execution time of our proposed method was 3.83 s for a 30 min ECG recording. It shows the capability of the classifier to detect abnormal PVCs in online manner.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

New law limits idling of diesel vehicles to reduce emissions and save fuel.

Diesel trucks and buses account for approximately 50 percent of the particulate matter (PM) and oxides of nitrogen (NOx) air pollution from on-road vehicles in Illinois. PM and NOx may contribute to a variety of health effects, including nausea, headaches, increased risk of asthma attacks, lung cancer, and premature death. Children and people with lung and heart conditions, are generally the most sensitive to diesel exhaust. Millions of tons of air pollution are emitted every year in the U.S. by trucks and buses that idle while parked.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

Saddle and camp in the Rockies; an expert's picture of game conditions in the heart of our hunting country,

Mode of access: Internet.