Hepatectomy with hypothermic perfusion of the liver

Whereas most liver resections can be performed within 60 min, the period of vascular clamping and resulting ischemia may prove too short to allow complex major liver resections (MLR) especially on diseased livers. To overcome this problem, cooling of the liver with 4 degrees C preservations solution routinely used in liver transplantation may be used in three different approaches to MLR: I "In situ": the liver remains in the abdomen and integrity of afferent and efferent vessels is conserved. II "Ex situ-in vivo": the liver exteriorized from the abdomen by transecting all hepatic veins, remains connected to the porta hepatis. III "Ex vivo": the liver being removed from the abdomen, the MLR is performed extracorporeally. Of 15 MLR reported here, 11 were performed "in situ" and 4 "ex situ-in vivo"/Nowadays, the liver surgeon's "toolbox" must contain hypothermic liver perfusion. In carefully selected cases, these techniques allow MLR on diseases livers or mandating complex vascular procedures.     

A rapid computational filter for predicting the rate of human renal clearance

In silico models that predict the rate of human renal clearance for a diverse set of drugs, that exhibit both active secretion and net re-absorption, have been produced using three statistical approaches. Partial Least Squares (PLS) and Random Forests (RF) have been used to produce continuous models whereas Classification And Regression Trees (CART) has only been used for a classification model. The best models generated from either PLS or RF produce significant models that can predict acids/zwitterions, bases and neutrals with approximate average fold errors of 3, 3 and 4, respectively, for an independent test set that covers oral drug-like property space. These models contain additional information on top of any influence arising from plasma protein binding on the rate of renal clearance. Classification And Regression Trees (CART) has been used to generate a classification tree leading to a simple set of Renal Clearance Rules (RCR) that can be applied to man. The rules are influenced by lipophilicity and ion class and can correctly predict 60% of an independent test set. These percentages increase to 71% and 79% for drugs with renal clearances of < 0.1 ml/min/kg and > 1 ml/min/kg, respectively. As far as the authors are aware these are the first set of models to appear in the literature that predict the rate of human renal clearance and can be used to manipulate molecular properties leading to new drugs that are less likely to fail due to renal clearance.     

Fundamental dynamics of flow through carbon nanotube membranes

The flow of a model non-polar liquid through small carbon nanotubes is studied using non-equilibrium molecular dynamics simulation. We explain how a membrane of small-diameter nanotubes can transport this liquid faster than a membrane consisting of larger-diameter nanotubes. This effect is shown to be back-pressure dependent, and the reasons for this are explored. The flow through the very smallest nanotubes is shown to depend strongly on the depth of the potential inside, suggesting atomic separation can be based on carbon interaction strength as well as physical size. Finally, we demonstrate how increasing the back-pressure can counter-intuitively result in lower exit velocities from a nanotube. Such studies are crucial for optimisation of nanotube membranes.     

An improved approximation to the precision of fixed effects from restricted maximum likelihood

An approximate small sample variance estimator for fixed effects from the multivariate normal linear model, together with appropriate inference tools based on a scaled F pivot, is now well established in practice and there is a growing literature on its properties in a variety of settings. Although effective under linear covariance structures, there are examples of nonlinear structures for which it does not perform as well. The cause of this problem is shown to be a missing term in the underlying Taylor series expansion which accommodates the bias in the estimators of the parameters of the covariance structure. The form of this missing term is derived, and then used to adjust the small sample variance estimator. The behaviour of the resulting estimator is explored in terms of invariance under transformation of the covariance parameters and also using a simulation study. It is seen to perform successfully in the way predicted from its derivation.     

Genetic adaptive state estimation

A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. This paper describes a GA that can perform on-line adaptive state estimation for linear and nonlinear systems. First, it shows how to construct a genetic adaptive state estimator where a GA evolves the model in a state estimator in real time so that the state estimation error is driven to zero. Next, several examples are used to illustrate the operation and performance of the genetic adaptive state estimator. Its performance is compared to that of the conventional adaptive Luenberger observer for two linear system examples. Next, a genetic adaptive state estimator is used to predict when surge and stall occur in a nonlinear jet engine. Our main conclusion is that the genetic adaptive state estimator has the potential to offer higher performance estimators for nonlinear systems over current methods.