Ru San Tan

The Twin Transfusion Syndrome: Clinical Observations on 35 Affected Pairs

bidity and mortality in twin pregnancies. 1-3 The weight difference between twin members presenting with this syndrome may be very marked in some instances; minimal in others. It has therefore been postulated that two types of this syndrome exist: a &dquo;chronic&dquo; form existing during pregnancy, and an &dquo;acute&dquo; form occurring only during parturitic~n.~ The large numbers of deliveries occurring in the Kandang Kerbau Hospital, Singapore, (about 25,000 annually) provided

Transplantation of Nanoparticle Transfected Skeletal Myoblasts Overexpressing Vascular Endothelial Growth Factor-165 for Cardiac Repair

Background— We investigated the feasibility and efficacy of polyethylenimine (PEI) based human vascular endothelial growth factor-165 (hVEGF165) gene transfer into human skeletal myoblasts (HSM) for cell based delivery to the infarcted myocardium. Methods and Results— Based on optimized transfection procedure using enhanced green fluorescent protein (pEGFP), HSM were transfected with plasmid-hVEGF165 (phVEGF165) carried by PEI (PEI- phVEGF165) nanoparticles. The transfected HSM were characterized for transfection and expression of hVEGF165 in vitro and transplanted into rat heart model of acute myocardial infarction (AMI): group-1=DMEM injection, group-2= HSM transplantation, group-3= PEI-phVEGF165–transfected HSM (PEI-phVEGF165 myoblast) transplantation. A total of 48 rats received cyclosporine injection from 3 days before and until 4 weeks after cell transplantation. Echocardiography was performed to assess the heart function. Animals were sacrificed for molecular and histological studies on the heart tissue at 4 weeks after treatment. Based on optimized transfection conditions, transfected HSM expressed hVEGF165 for 18 days with >90% cell viability in vitro. Apoptotic index was reduced in group-2 and group-3 as compared with group-1. Blood vessel density (×400) by immunostaining for PECAM-1 in group-3 was significantly higher (P=0.043 for both) as compared with group-1 and group-2 at 4 weeks. Regional blood flow (ml/min/g) in the left ventricular anterior wall was higher in group-3 (P=0.043 for both) as compared with group-1 and group-2. Improved ejection fraction was achieved in group-3 (58.44±4.92%) as compared with group-1 (P=0.004). Conclusion— PEI nanoparticle mediated hVEGF165 gene transfer into HSM is feasible and safe. It may serve as a novel and efficient alternative for angiomyogenesis in cardiac repair.

Right ventricular regional wall curvedness and area strain in patients with repaired tetralogy of Fallot.

A quantitative understanding of right ventricular (RV) remodeling in repaired tetralogy of Fallot (rTOF) is crucial for patient management. The objective of this study is to quantify the regional curvatures and area strain based on three-dimensional (3-D) reconstructions of the RV using cardiac magnetic resonance imaging (MRI). Fourteen (14) rTOF patients and nine (9) normal subjects underwent cardiac MRI scan. 3-D RV endocardial surface models were reconstructed from manually delineated contours and correspondence between end-diastole (ED) and end systole (ES) was determined. Regional curvedness (C) and surface area at ED and ES were calculated as well as the area strain. The RV shape and deformation in rTOF patients differed from normal subjects in several respects. Firstly, the curvedness at ED (mean for 13 segments, 0.030 ± 0.0076 vs. 0.029 ± 0.0065 mm(-1); P < 0.05) and ES (mean for 13 segments, 0.040 ± 0.012 vs. 0.034 ± 0.0072 mm(-1); P < 0.001) was decreased by chronic pulmonary regurgitation. Secondly, the surface area increased significantly at ED (mean for 13 segments, 982 ± 192 vs. 1,397 ± 387 mm(2); P < 0.001) and ES (mean for 13 segments, 576 ± 130 vs. 1,012 ± 302 mm(2); P < 0.001). In particular, rTOF patients had significantly larger surface area than that in normal subjects in the free wall but not for the septal wall. Thirdly, area strain was significantly decreased (mean for 13 segments, 56 ± 6 vs. 34 ± 7%; P < 0.0001) in rTOF patients. Fourthly, there were increases in surface area at ED (5,726 ± 969 vs. 6,605 ± 1,122 mm(2); P < 0.05) and ES (4,280 ± 758 vs. 5,569 ± 1,112 mm(2); P < 0.01) and decrease in area strain (29 ± 8 vs. 18 ± 8%; P < 0.001) for RV outflow tract. These findings suggest significant geometric and strain differences between rTOF and normal subjects that may help guide therapeutic treatment.

An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features

Display Omitted Novel Sudden Cardiac Death Index (SCDI) is proposed using ECG signals.Nonlinear features are extracted from DWT coefficients.SCDI is formulated using nonlinear features.SCDI predicts accurately SCD 4min before its onset. Early prediction of person at risk of Sudden Cardiac Death (SCD) with or without the onset of Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF) still remains a continuing challenge to clinicians. In this work, we have presented a novel integrated index for prediction of SCD with a high level of accuracy by using electrocardiogram (ECG) signals. To achieve this, nonlinear features (Fractal Dimension (FD), Hursts exponent (H), Detrended Fluctuation Analysis (DFA), Approximate Entropy (ApproxEnt), Sample Entropy (SampEnt), and Correlation Dimension (CD)) are first extracted from the second level Discrete Wavelet Transform (DWT) decomposed ECG signal. The extracted nonlinear features are ranked using t-value and then, a combination of highly ranked features are used in the formulation and employment of an integrated Sudden Cardiac Death Index (SCDI). This calculated novel SCDI can be used to accurately predict SCD (four minutes before the occurrence) by using just one numerical value four minutes before the SCD episode. Also, the nonlinear features are fed to the following classifiers: Decision Tree (DT), k-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The combination of DWT and nonlinear analysis of ECG signals is able to predict SCD with an accuracy of 92.11% (KNN), 98.68% (SVM), 93.42% (KNN) and 92.11% (SVM) for first, second, third and fourth minutes before the occurrence of SCD, respectively. The proposed SCDI will constitute a valuable tool for the medical professionals to enable them in SCD prediction.

Impact of surgical ventricular restoration on ventricular shape, wall stress, and function in heart failure patients

Surgical ventricular restoration (SVR) was designed to treat patients with aneurysms or large akinetic walls and dilated ventricles. Yet, crucial aspects essential to the efficacy of this procedure like optimal shape and size of the left ventricle (LV) are still debatable. The objective of this study is to quantify the efficacy of SVR based on LV regional shape in terms of curvedness, wall stress, and ventricular systolic function. A total of 40 patients underwent magnetic resonance imaging (MRI) before and after SVR. Both short-axis and long-axis MRI were used to reconstruct end-diastolic and end-systolic three-dimensional LV geometry. The regional shape in terms of surface curvedness, wall thickness, and wall stress indexes were determined for the entire LV. The infarct, border, and remote zones were defined in terms of end-diastolic wall thickness. The LV global systolic function in terms of global ejection fraction, the ratio between stroke work (SW) and end-diastolic volume (SW/EDV), the maximal rate of change of pressure-normalized stress (dσ*/dt(max)), and the regional function in terms of surface area change were examined. The LV end-diastolic and end-systolic volumes were significantly reduced, and global systolic function was improved in ejection fraction, SW/EDV, and dσ*/dt(max). In addition, the end-diastolic and end-systolic stresses in all zones were reduced. Although there was a slight increase in regional curvedness and surface area change in each zone, the change was not significant. Also, while SVR reduced LV wall stress with increased global LV systolic function, regional LV shape and function did not significantly improve.

Improved angiogenic response in pig heart following ischaemic injury using human skeletal myoblast simultaneously expressing VEGF165 and angiopoietin-1.

To achieve angiogenic interaction between VEGF165 and angiopoietin‐1 (Ang‐1) using a novel adenoviral bicistronic vector (Ad‐Bic) encoding the two factors and delivered ex vivo using sex‐mismatched human skeletal myoblasts.

Automated detection and localization of myocardial infarction using electrocardiogram

Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy ( E a x ), signal energy (?x), fuzzy entropy ( E f x ), Kolmogorov-Sinai entropy ( E k s x ), permutation entropy ( E p x ), Renyi entropy ( E r x ), Shannon entropy ( E s h x ), Tsallis entropy ( E t s x ), wavelet entropy ( E w x ), fractal dimension ( F D x ), Kolmogorov complexity ( C k x ), and largest Lyapunov exponent ( E L L E x ) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k-nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V5). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V3). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V3) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision.

Therapeutic angiogenesis by transplantation of human embryonic stem cell-derived CD133+ endothelial progenitor cells for cardiac repair

OBJECTIVE This study aim to enhance endothelial differentiation of human embryonic stem cells (hESCs) by transduction of an adenovirus (Ad) vector expressing hVEGF(165) gene (Ad-hVEGF(165) ). Purified hESC-derived CD133(+) endothelial progenitors were transplanted into a rat myocardial infarct model to assess their ability to contribute to heart regeneration. METHODS Optimal transduction efficiency with high cell viability was achieved by exposing differentiating hESCs to viral particles at a ratio of 1:500 for 4 h on three consecutive days. RESULTS Reverse transcription-PCR analysis showed positive upregulation of VEGF, Ang-1, Flt-1, Tie-2, CD34, CD31, CD133 and Flk-1 gene expression in Ad-hVEGF(165) -transduced cells. Additionally, flow cytometric analysis of CD133, a cell surface marker, revealed an approximately fivefold increase of CD133 marker expression in Ad-hVEGF(165)-transduced cells compared with the nontransduced control. Within a rat myocardial infarct model, transplanted CD133(+) endothelial progenitor cells survived and participated, both actively and passively, in the regeneration of the infarcted myocardium, as seen by an approximately threefold increase in mature blood vessel density (13.62 +/- 1.56 vs 5.11 +/- 1.23; p < 0.01), as well as significantly reduced infarct size (28% +/- 8.2% vs 76% +/- 5.6%; p < 0.01) in the transplanted group compared with the culture medium-injected control. There was significant improvement in heart function 6 weeks post-transplantation, as confirmed by regional blood-flow analysis (1.72 +/- 0.612 ml/min/g vs 0.8 +/- 0.256 ml/min/g; p < 0.05), as well as echocardiography assessment of left ventricular ejection fraction (60.855% +/- 7.7% vs 38.22 +/- 8.6%; p < 0.05) and fractional shortening (38.63% +/- 9.3% vs 25.2% +/- 7.11%; p < 0.05). CONCLUSION hESC-derived CD133(+) endothelial progenitor cells can be utilized to regenerate the infarcted heart.

Effects of Surgical Ventricular Restoration on Left Ventricular Contractility Assessed by a Novel Contractility Index in Patients With Ischemic Cardiomyopathy

A pressure-normalized left ventricular (LV) wall stress (dsigma*/dt(max)) was recently reported as a load-independent index of LV contractility. We hypothesized that this novel contractility index might demonstrate improvement in LV contractile function after surgical ventricular restoration (SVR) using magnetic resonance imaging. A retrospective analysis of magnetic resonance imaging data of 40 patients with ischemic cardiomyopathy who had undergone coronary artery bypass grafting with SVR was performed. LV volumes, ejection fraction, global systolic and diastolic sphericity, and dsigma*/dt(max) were calculated. After SVR, a decrease was found in end-diastolic and end-systolic volume indexes, whereas LV ejection fraction increased from 26% +/- 7% to 31% +/- 10% (p <0.001). LV mass index and peak normalized wall stress were decreased, whereas the sphericity index (SI) at end-diastole increased, indicating that the left ventricle became more spherical after SVR. LV contractility index dsigma*/dt(max) improvement (from 2.69 +/- 0.74 to 3.23 +/- 0.73 s(-1), p <0.001) was associated with shape change as evaluated by the difference in SI between diastole and systole (r = 0.32, p <0.001, preoperative; r = 0.23, p <0.001, postoperative), but not with baseline LV SI. In conclusion, SVR excludes akinetic LV segments and decreases LV wall stress. Despite an increase in sphericity, LV contractility, as determined by dsigma*/dt(max), actually improves. A complex interaction of LV maximal flow rate and LV mass may explain the improvement in LV contractility after SVR. Because dsigma*/dt(max) can be estimated from simple noninvasive measurements, this underscores its clinical utility for assessment of contractile function with therapeutic intervention.

Numerical simulation of patient-specific left ventricular model with both mitral and aortic valves by FSI approach

Intraventricular flow is important in understanding left ventricular function; however, relevant numerical simulations are limited, especially when heart valve function is taken into account. In this study, intraventricular flow in a patient-specific left ventricle has been modelled in two-dimension (2D) with both mitral and aortic valves integrated. The arbitrary Lagrangian-Eulerian (ALE) approach was employed to handle the large mesh deformation induced by the beating ventricular wall and moving leaflets. Ventricular wall deformation was predefined based on MRI data, while leaflet dynamics were predicted numerically by fluid-structure interaction (FSI). Comparisons of simulation results with in vitro and in vivo measurements reported in the literature demonstrated that numerical method in combination with MRI was able to predict qualitatively the patient-specific intraventricular flow. To the best of our knowledge, we are the first to simulate patient-specific ventricular flow taking into account both mitral and aortic valves.