scholarly journals Pertussis toxin treatment of whole blood. A novel approach to assess G protein function in congestive heart failure.

Circulation ◽  
1990 ◽  
Vol 81 (4) ◽  
pp. 1198-1204 ◽  
Author(s):  
A S Maisel ◽  
M C Michel ◽  
P A Insel ◽  
C Ennis ◽  
M G Ziegler ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
G Protein ◽  
Pertussis Toxin ◽  
Whole Blood ◽  
Protein Function ◽  
Novel Approach

Treatment of Three Cats with Hyperviscosity Syndrome and Congestive Heart Failure Using Plasmapheresis

2011 ◽  
Vol 47 (1) ◽  
pp. 50-55 ◽  
Author(s):  
Tonya E. Boyle ◽  
Marie K. Holowaychuk ◽  
Allison K. Adams ◽  
Steven L. Marks
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Plasma Cell ◽  
Whole Blood ◽  
Clinical Signs ◽  
Post Mortem ◽  
Respiratory Effort ◽  
Hyperviscosity Syndrome ◽  
Heart Murmurs ◽  
Appropriate Therapy

Three cats were evaluated at a veterinary teaching hospital for congestive heart failure (CHF) secondary to hyperviscosity syndrome from plasma cell neoplasia. All cats had severe hyperproteinemia due to hyperglobulinemia. Multiple myeloma or plasma cell neoplasia was diagnosed based on cytopathology and post mortem examination. The cats presented with signs of CHF including acute collapse, tachypnea, increased respiratory effort, and pulmonary crackles. All cats had heart murmurs and echocardiographic signs consistent with hypertrophic cardiomyopathy. An enlarged left atrium was found in all cats and two of three cats also had spontaneous echocardiographic contrast. Plasmapheresis (centrifugal plasma exchange) was performed on all three cats by the removal of whole blood and the infusion of a balanced electrolyte solution while the whole blood was centrifuged and separated. The RBCs were then washed before being readministered to the patient. Plasmapheresis alleviated the clinical signs of CHF (tachypnea) in all three cats. Plasmapheresis should be considered in cases of CHF secondary to hyperviscosity syndrome to rapidly alleviate clinical signs associated with heart failure while diagnosis of the underlying cause is made and appropriate therapy implemented.

Expression of mRNA encoding G protein-coupled receptors involved in congestive heart failure

2007 ◽  
Vol 102 (3) ◽  
pp. 198-208 ◽  
Author(s):  
T. Brattelid ◽  
K. Tveit ◽  
J. A. K. Birkeland ◽  
I. Sjaastad ◽  
E. Qvigstad ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
G Protein ◽  
G Protein Coupled Receptors ◽  
G Protein Coupled

scholarly journals Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1169
Author(s):  
Mingjing Chen ◽  
Aodi He ◽  
Kaicheng Feng ◽  
Guanzheng Liu ◽  
Qian Wang
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Empirical Mode Decomposition ◽  
Transfer Entropy ◽  
Intrinsic Mode Functions ◽  
Rr Interval ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Novel Method ◽  
Screening Accuracy

Congestive heart failure (CHF) is a cardiovascular disease related to autonomic nervous system (ANS) dysfunction and fragmented patterns. There is a growing demand for assessing CHF accurately. In this work, 24-h RR interval signals (the time elapsed between two successive R waves of the QRS signal on the electrocardiogram) of 98 subjects (54 healthy and 44 CHF subjects) were analyzed. Empirical mode decomposition (EMD) was chosen to decompose RR interval signals into four intrinsic mode functions (IMFs). Then transfer entropy (TE) was employed to study the information transaction among four IMFs. Compared with the normal group, significant decrease in TE (*→1; information transferring from other IMFs to IMF1, p < 0.001) and TE (3→*; information transferring from IMF3 to other IMFs, p < 0.05) was observed. Moreover, the combination of TE (*→1), TE (3→*) and LF/HF reached the highest CHF screening accuracy (85.7%) in IBM SPSS Statistics discriminant analysis, while LF/HF only achieved 79.6%. This novel method and indices could serve as a new way to assessing CHF and studying the interaction of the physiological phenomena. Simulation examples and transfer entropy applications are provided to demonstrate the effectiveness of the proposed EMD decomposition method in assessing CHF.

scholarly journals Loss of endothelial pertussis toxin-sensitive G protein function in atherosclerotic porcine coronary arteries.

Circulation ◽  
1991 ◽  
Vol 83 (2) ◽  
pp. 652-660 ◽  
Author(s):  
H Shimokawa ◽  
N A Flavahan ◽  
P M Vanhoutte
Keyword(s):  
G Protein ◽  
Coronary Arteries ◽  
Pertussis Toxin ◽  
Protein Function

scholarly journals Whole blood impedance aggregometry for the assessment of platelet function in patients with congestive heart failure (EPCOT Trial)

2002 ◽  
Vol 4 (4) ◽  
pp. 461-467 ◽  
Author(s):  
V.L. Serebruany ◽  
M.E. McKenzie ◽  
A.F. Meister ◽  
S.Y. Fuzaylov ◽  
P.A. Gurbel ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Platelet Function ◽  
Whole Blood ◽  
Impedance Aggregometry ◽  
Blood Impedance

scholarly journals Diagnosis of Congestive Heart Failure from HRV signal using SVM classifier and Patient Specific cross validation

2020 ◽  
Vol 9 (3) ◽  
pp. 933-938
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Cross Validation ◽  
Research Work ◽  
Patient Specific ◽  
Support Vector ◽  
Svm Classifier ◽  
Largest Lyapunov Exponent ◽  
Detection Scheme ◽  
Novel Approach

Congestive heart failure (CHF) is popularly known fatal cardiac disease that occurs when pumping action of heart is lower than normal case. The purpose of this study is to the accurate diagnosis of CHF by improving classifier performance with effective features extraction and cross validation approach. The identification of significant features in electrocardiogram is highly important to detect congestive heart failure. Therefore ,this paper introduces a classifier based automated detection scheme with a novel approach of feature extraction from Heart rate Variability (HRV) signal for early prediction of CHF. The dynamical characteristics of HRV signal is analysed by computation of Largest Lyapunov Exponent. The statistical features are also evaluated to capture crucial variation in HRV signal to distinguish the abnormal and normal heart condition. The extracted features are subjected to Support Vector machine (SVM) classifier for automated discrimination of CHF from normal ECG signal. Experimental results evaluate the performance of extracted features and estimate the accuracy of the classification using the 10 fold cross validation and patient specific cross validation approach. Our experiment is validated by ECG data of normal and CHF subjects from Physionet database. The proposed system is efficient to the detect CHF with an average accuracy of 98.75%,, sensitivity 98.38%, values and 98.94%. Based on comparative study with the existing scientific research work to diagnose CHF, our proposed approach is found to be reliable and efficient for CHF diagnosis

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