Tanita T, Koike K, Fujimura S, Staub N C
Department of Surgery, Tohoku University, Sendai.
Tohoku J Exp Med. 1990 Mar;160(3):277-84. doi: 10.1620/tjem.160.277.
The filtration variables, K (filtration coefficient), Ppmv (perimicrovascular pressure) and sigma (reflection coefficient), were estimated independently in previous reports using the Starling equation or the micropuncture method. We used matrix algebra to estimate these variables simultaneously. We measured filtration rate (Q) by a gravimetric method in isolated rat lung lobes in zone 1 conditions (alveolar pressure = 20 cmH2O) at two vascular pressures, Pvasc = 15 or 18 cm H2O and perfused the lobes with plasma containing a low or a high concentration of protein. By extrapolating the log of the rate of weight gain to t = 0, we obtain the initial filtration rate before any of the pressure variables (microvascular and perimicrovascular hydrostatic pressures) in the Starling equation changed. Assuming that protein filtered into perimicrovascular space only by convection, we substituted it into the Starling equation as follows: Q = K [(Pmv -- Ppmv) -- sigma 2 (IImv)], where Pmv and IImv are microvascular and perimicrovascular plasma protein osmotic pressures. IImv was estimated by Yamada's equation (Yamada et al. 1985). For the matrix algebra, we used three values, we omitted the value for the high protein, low vascular pressure experiment. We obtained K = 26.3 [mg/(min x cmH2O x g wet weight)], Ppmv = 6.2 cmH2O and sigma = 0.46. These values agree with values from previous reports. Since these 3 filtration variables are interrelated, this new method for simultaneous measurement is more accurate than independent measurements are. The chief advantage of this method is that it does not require a separate estimate of isogravimetric pressure or a direct measurement of interstitial pressure, and all variables are obtained simultaneously.
在先前的报告中，过滤变量K（过滤系数）、Ppmv（微血管周围压力）和σ（反射系数）是分别使用斯塔林方程或微穿刺法估算的。我们使用矩阵代数同时估算这些变量。我们在区域1条件下（肺泡压 = 20 cmH₂O），通过重量法在分离的大鼠肺叶中测量过滤速率（Q），血管压力为Pvasc = 15或18 cmH₂O，并使用含低蛋白或高蛋白浓度的血浆灌注肺叶。通过将体重增加速率的对数外推至t = 0，我们得到了斯塔林方程中任何压力变量（微血管和微血管周围静水压力）改变之前的初始过滤速率。假设蛋白质仅通过对流滤入微血管周围间隙，我们将其代入斯塔林方程如下：Q = K[(Pmv - Ppmv) - σ²(IImv)]，其中Pmv和IImv分别是微血管和微血管周围血浆蛋白渗透压。IImv通过山田方程估算（Yamada等人，1985年）。对于矩阵代数，我们使用了三个值，省略了高蛋白、低血管压力实验的值。我们得到K = 26.3 [mg/(min·cmH₂O·g湿重)]，Ppmv = 6.2 cmH₂O，σ = 0.46。这些值与先前报告中的值一致。由于这三个过滤变量相互关联，这种同时测量的新方法比单独测量更准确。该方法的主要优点是不需要单独估算等重力压力或直接测量间质压力，并且所有变量是同时获得的。
