Afterload reduction in the management of postinfarction ventricular septal defect

The primary goal in the medical management of ventricular septal defect complicating myocardial infarction is to support cardiac function and control symptoms, if possible, for a period of 4 to 6 weeks. If the patient survives this period, surgical correction of the defect is technically easier and safer. In many cases, However, cardiac function is severly compromised, intractable biventricular failure develops,early operation is necessary and the likelihood of successful repair is diminished.     

Clustered components analysis for functional MRI

A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels. Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method. This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques. A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.     

Can we learn anything from single-channel unaveraged MEG data?

A method for the decomposition of single-channel unaveraged magnetoencephalographic (MEG) data into statistically independent components is presented. The study of MEG recordings is characterised by a host of difficulties, most of which stem from the inherently noisy recording process by which the data is obtained. MEG time series typically contain a mix of artifactual components from a variety of sources, and the isolation of interesting signals from this noise background poses a difficult problem. In this article, we present a novel approach combining the techniques of independent component analysis (ICA) and dynamical embedding, which can be used to extract and isolate components of interest from single-channel unaveraged MEG data. In our approach, the method of delays is proposed as a means of augmenting the single-channel data, thus, facilitating the application of ICA. Finally, because the single-channel approach yields no information regarding the physiological origins of extracted sources, we discuss a method by which extracted sources may be projected back into the multichannel measurement space, permitting an estimate of the respective spatial distributions to be obtained. The proposed methods are tested on three separate MEG channels and the results are presented and discussed.     

Metabolite-sensitive holographic biosensors

A new type of biosensor that combines the inexpensiveness and mass-produceability of reflection holograms with the selectivity and specificity of enzymes is described. pH-sensitive holographic sensors were fabricated from ionizable monomers incorporated into thin, polymeric, hydrogel films which were transformed into volume holograms using a diffusion method coupled with holographic recording, using a frequency-doubled Nd:YAG laser (532 nm). These holograms were used as transducer systems to monitor the pH changes associated with specific enzymatic reactions to construct prototype urea- and penicillin-sensitive biosensors. The diffraction wavelength (color) of the holographic biosensors was used to characterize their shrinkage and swelling behavior as a function of analyte concentration. The potential of these sensors for the measurement of the clinically and industrially important metabolites urea and penicillin G is demonstrated.     

Glucose-sensitive holographic sensors for monitoring bacterial growth

A glucose sensor comprising a reflection hologram incorporated into a thin, acrylamide hydrogel film bearing the cis-diol binding ligand, 3-acrylamidophenylboronic acid (3-APB), is described. The diffraction wavelength (color) of the hologram changes as the polymer swells upon binding cis-diols. The effect of various concentrations of glucose, a variety of mono- and disaccharides, and the alpha-hydroxy acid, lactate, on the holographic response was investigated. The sensor displayed reversible changes in diffraction wavelength as a function of cis-diol concentration, with the sensitivity of the system being dependent on the cis-diol tested. The effect of varying 3-APB concentration in the hydrogel on the holographic response to glucose was investigated, and maximum sensitivity was observed at a functional monomer concentration of 20 mol %. The potential for using this holographic sensor to detect real-time changes in bacterial cell metabolism was demonstrated by monitoring the germination and subsequent vegetative growth of Bacillus subtilis spores.