Hemodynamic Parameters Influencing Clinical Performance of Novacor Left Ventricular Assist System

The interrelationships between hemodynamic variables including right ventricular (RV) performance with filling/ejection dynamics of the Novacor left ventricular assist system (LVAS) were determined in 10 of 11 patients who received LVAS as a bridge to heart transplant. Nine were successfully transplanted. Data were obtained intraoperatively, at periodic intervals up to 48 h postimplant and at explant. The hypotheses investigated included (a) RV performance influences LVAS filling characteristics and (b) LVAS pump output is influenced by systemic vascular resistance (SVR). During the period of LVAS support (2-126 days), pumping characteristics included a mean filling volume of 51 ml (range, 24-70), residual volume of 4.9 ml (range, 1-18), pump rate of 113/min (range, 63-175), and pump output of 5.81/min (range, 2.8-8.2). Multiple regression analysis identified pulmonary vascular resistance (PVR), RV stroke work index (RVSWI), and pulmonary capillary wedge pressure, but not RV ejection fraction, pulmonary artery pressure, or central venous pressure (CVP) as the most important correlates with LVAS filling volume (p less than 0.001, R2 = 0.6). In addition, LVAS pump output was influenced mainly by RVSWI, PVR, and SVR (p less than 0.001, R2 = 0.7). It was concluded that LVAS performance is highly dependent on RV function and systemic/pulmonary vascular resistances.     

Real-time PCR检测风疹病毒方法学建立及临床应用

为了建立一种能广泛应用于临床快速、简便、灵敏度和特异性较好的风疹病毒(RV)定量诊断方法。针对RV基因保守序列设计两对引物和一条荧光双标记探针。将PCR扩增所得到的产物片段克隆,作为定量检测的标准品,进行Real-time PCR检测,绘制标准曲线。将此方法和ELISA试剂盒平行检测50份孕妇血清,评估两种方法检出阳性率的差异显著性。Real-time PCR法能较好地检出RVcDNA载量。曲线的相关系数(r)为0.998,可检测线性范围大约在103~109copies/μl,灵敏度接近103copies/μl;批间、批内CV值分别为3.36%、0.94%;也有较好的特异性。与经典的ELISA方法相比,有显著性差异。Real-time PCR方法操作简单、快速,并能避免PCR后处理导致的假阳性污染,实现实时定量。此方法对RV感染的临床诊断和疗效判断等方面有较大的指导意义。     

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a “context penalty” that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method’s robustness to noise and inaccurate segmentations.