Application of Signal Advance Technology to Electrophysiology

Medical instrumentation used in diagnosis and treatment relies on the accurate detection and processing of various physiological events and signals. While signal detection technology has improved greatly in recent years, there remain inherent delays in signal detection/ processing. These delays may have significant negative clinical consequences during various pathophysiological events. Reducing or eliminating such delays would increase the ability to provide successful early intervention in certain disorders thereby increasing the efficacy of treatment. In recent years, a physical phenomenon referred to as Negative Group Delay (NGD), demonstrated in simple electronic circuits, has been shown to temporally advance the detection of analog waveforms. Specifically, the output is temporally advanced relative to the input, as the time delay through the circuit is negative. The circuit output precedes the complete detection of the input signal. This process is referred to as signal advance (SA) detection. An SA circuit model incorporating NGD was designed, developed and tested. It imparts a constant temporal signal advance over a pre-specified spectral range in which the output is almost identical to the input signal (i.e., it has minimal distortion). Certain human patho-electrophysiological events are good candidates for the application of temporally-advanced waveform detection. SA technology has potential in early arrhythmia and epileptic seizure detection and intervention. Demonstrating reliable and consistent temporally advanced detection of electrophysiological waveforms may enable intervention with a pathological event (much) earlier than previously possible. SA detection could also be used to improve the performance of neural computer interfaces, neurotherapy applications, radiation therapy and imaging. In this study, the performance of a single-stage SA circuit model on a variety of constructed input signals, and human ECGs is investigated. The data obtained is used to quantify and characterize the temporal advances and circuit gain, as well as distortions in the output waveforms relative to their inputs. This project combines elements of physics, engineering, signal processing, statistics and electrophysiology. Its success has important consequences for the development of novel interventional methodologies in cardiology and neurophysiology as well as significant potential in a broader range of both biomedical and non-biomedical areas of application.

Detection of nonrandom association of alleles from the distribution of the number of heterozygous loci in a sample.

The distribution of the number of heterozygous loci in two randomly chosen gametes or in a random diploid zygote provides information regarding the nonrandom association of alleles among different genetic loci. Two alternative statistics may be employed for detection of nonrandom association of genes of different loci when observations are made on these distributions: observed variance of the number of heterozygous loci (s2k) and a goodness-of-fit criterion (X2) to contrast the observed distribution with that expected under the hypothesis of random association of genes. It is shown, by simulation, that s2k is statistically more efficient than X2 to detect a given extent of nonrandom association. Asymptotic normality of s2k is justified, and X2 is shown to follow a chi-square (chi 2) distribution with partial loss of degrees of freedom arising because of estimation of parameters from the marginal gene frequency data. Whenever direct evaluations of linkage disequilibrium values are possible, tests based on maximum likelihood estimators of linkage disequilibria require a smaller sample size (number of zygotes or gametes) to detect a given level of nonrandom association in comparison with that required if such tests are conducted on the basis of s2k. Summarization of multilocus genotype (or haplotype) data, into the different number of heterozygous loci classes, thus, amounts to appreciable loss of information.

Detection of enterotoxigenic Escherichia coli in foods by polymerase chain reaction

Enterotoxigenic Escherichia coli (ETEC) causes significant morbidity and mortality in infants of developing countries and is the most common cause of diarrhea in travelers to these areas. Enterotoxigenic Escherichia coli infections are commonly caused by ingestion of fecally contaminated food. A timely method for the detection of ETEC in foods would be important in the prevention of this disease. A multiplex polymerase chain reaction (PCR) assay which has been successful in detecting the heat-labile and heat-stable toxins of ETEC in stool was examined to determine its utility in foods. This PCR assay, preceded by a glass matrix and chaotropic DNA extraction, was effective in detecting high numbers of ETEC in a variety of foods. Ninety percent of 121 spiked food samples yielded positive results. Samples of salsa from Guadalajara, Mexico and Houston, Texas were collected and underwent DNA extraction and PCR. All samples yielded negative results for both the heat-labile and heat-stable toxins. Samples were also subjected to oligonucleotide probe analysis and resulted in 5 samples positive for ETEC. Upon dilution testing, it was found that positive PCR results only occurred when 12,000 to 1,000,000 bacteria were present in 200 mg of food. Although the DNA extraction and PCR method has been shown to be both sensitive and specific in stool, similar results were not obtained in food samples. ^