Designing biostable polyurethane elastomers for biomedical implants

The chemical structure, synthesis, morphology, and properties of polyurethane elastomers are briefly discussed. The current understanding of the effect of chemical structure and the associated morphology on the stability of polyurethanes in the biological environments is reviewed. The degradation of conventional polyurethanes appears as surface or deep cracking, stiffening, and deterioration of mechanical properties, such as flex-fatigue resistance. Polyester and poly( tetramethylene oxide) based polyurethanes degrade by hydrolytic and oxidative degradation of ester and ether functional groups, respectively. The recent approaches to develop polyurethanes with improved long-term biostability are based on developing novel polyether, hydrocarbon, polycarbonate, and siloxane macrodiols to replace degradation-prone polyester and polyether macrodiols in polyurethane formulations. The new approaches are discussed with respect to synthesis, properties and biostability based on reported in vivo studies. Among the newly developed materials, siloxane-based polyurethanes have exhibited excellent biostability and are expected to find many applications in biomedical implants.

Methacryloxyethyl phosphate-grafted expanded polytetrafluoroethylene membranes for biomedical applications

Expanded polytetrafluoroethylene (ePTFE) membranes were modified by graft copolymerization with methacryloxyethyl phosphate (MOEP) in methanol and 2-butanone (methyl ethyl ketone (MEK)) at ambient temperature using gamma irradiation. The effect of dose rate (0.46 and 4.6 kGyh(-1)), monomer concentration (1-40 %) and solvent were studied and the modified membranes were characterized by weight increase, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). XPS was used to determine the % degree of surface coverage using the C-F (ePTFE membrane) and the C-C (MOEP graft copolymer) peaks. Grafting yield, as well as surface coverage, were found to increase with increasing monomer concentration and were significantly higher for samples grafted in MEK than in methanol solution. SEM images showed distinctly different surface morphologies for the membranes grafted in methanol (smooth) and MEK (globular), hence indicating phase separation of the homopolymer in MEK. We propose that in our system, the non-solvent properties of MEK for the homopolymer play a more important role than solvent chain transfer reactions in determining grafting outcomes. (c) 2005 Society of Chemical Industry.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Electrodynamic heart model construction and ECG simulation

Objectives: In this paper, we present a unified electrodynamic heart model that permits simulations of the body surface potentials generated by the heart in motion. The inclusion of motion in the heart model significantly improves the accuracy of the simulated body surface potentials and therefore also the 12-lead ECG. Methods: The key step is to construct an electromechanical heart model. The cardiac excitation propagation is simulated by an electrical heart model, and the resulting cardiac active forces are used to calculate the ventricular wall motion based on a mechanical model. The source-field point relative position changes during heart systole and diastole. These can be obtained, and then used to calculate body surface ECG based on the electrical heart-torso model. Results: An electromechanical biventricular heart model is constructed and a standard 12-lead ECG is simulated. Compared with a simulated ECG based on the static electrical heart model, the simulated ECG based on the dynamic heart model is more accordant with a clinically recorded ECG, especially for the ST segment and T wave of a V1-V6 lead ECG. For slight-degree myocardial ischemia ECG simulation, the ST segment and T wave changes can be observed from the simulated ECG based on a dynamic heart model, while the ST segment and T wave of simulated ECG based on a static heart model is almost unchanged when compared with a normal ECG. Conclusions: This study confirms the importance of the mechanical factor in the ECG simulation. The dynamic heart model could provide more accurate ECG simulation, especially for myocardial ischemia or infarction simulation, since the main ECG changes occur at the ST segment and T wave, which correspond with cardiac systole and diastole phases.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.