Identification of patients at highest risk for ventilator-associated pneumonia in the surgical intensive care unit.

Ventilator-acquired pneumonia (VAP) is a major problem for patients admitted to the surgical intensive care unit mechanically ventilated. Recently, we have identified both clinical and immunologic factors associated with the development of VAP. The clinical risk factors are associated with the severity of the injury and the length of mechanical ventilation. The immunologic risk factors are associated with the local lung inflammatory response that is unchecked and affects local cell function. The combination of the severity of injury, the length of mechanical ventilation, and the failure to "auto-regulate" the lung response places the host at risk of VAP. In the next millennium, if we are to make significant advances in the management of VAP, we will need to understand the pathophysiology of the disease process. Then we can develop preventive strategies that will reduce the morbidity and the associated cost of VAP.

Measurements of nanometer scale interface diffusion between tungsten and niobium thin films using high frequency laser based ultrasound

This article presents the use of a quantitative analysis technique to describe time-resolved acoustic spectroscopy (high frequency laser based ultrasound) measurements of atomic diffusion on nanometer length scales occurring at the interface between sputter-deposited tungsten and niobium films. The extent of diffusion at the tungsten-niobium interface is determined by comparing experimental, simulated, and theoretical transfer functions between acoustic arrivals. The experimental and simulated transfer functions use the spectral content of successive reflected acoustic waves and the theoretical transfer function is based on the transfer matrix of an equivalent stratified interface region. This combination of theoretical, simulated, and experimental analyses makes it possible to separate signals with distinct differences between the as-deposited interface and those interfaces diffused to an experimentally determined 0.8-nm and 1.4-nm extent. Comparison of predicted and measured diffusion depths for this diffusion couple indicates that bulk diffusivities are not appropriate for describing nanometer scale interface diffusion.

Prediction of Phase Behavior in Microemulsion Systems Using Artificial Neural Networks

Preliminary investigations have been conducted to assess the potential for using (back-propagation, feed-forward) artificial neural networks to predict the phase behavior of quaternary microemulsion-forming systems, with a view to employing this type of methodology in the evaluation of novel cosurfactants for the formulation of pharmaceutically acceptable drug-delivery systems. The data employed in training the neural networks related to microemulsion systems containing lecithin, isopropyl myristate, and water, together with different types of cosurfactants, including short- and medium-chain alcohols, amines, acids, and ethylene glycol monoalkyl ethers. Previously unpublished phase diagrams are presented for four systems involving the cosurfactants 2-methyl-2-butanol, 2-methyl-1-propanol, 2-methyl-1-butanol, and isopropanol, which, along with eight other published sets of data, are used to test the predictive ability of the trained networks. The pseudo-ternary phase diagrams for these systems are predicted using only four computed physicochemical properties for the cosurfactants involved. The artificial neural networks are shown to be highly successful in predicting phase behavior for these systems, achieving mean success rates of 96.7 and 91.6% for training and test data, respectively. The conclusion is reached that artificial neural networks can provide useful tools for the development of microemulsion-based drug-delivery systems.