3 resultados para Sample Preparation

em DigitalCommons@The Texas Medical Center


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Dielectrophoresis (DEP) has been used to manipulate cells in low-conductivity suspending media using AC electrical fields generated on micro-fabricated electrode arrays. This has created the possibility of performing automatically on a micro-scale more sophisticated cell processing than that currently requiring substantial laboratory equipment, reagent volumes, time, and human intervention. In this research the manipulation of aqueous droplets in an immiscible, low-permittivity suspending medium is described to complement previous work on dielectrophoretic cell manipulation. Such droplets can be used as carriers not only for air- and water-borne samples, contaminants, chemical reagents, viral and gene products, and cells, but also the reagents to process and characterize these samples. A long-term goal of this area of research is to perform chemical and biological assays on automated, micro-scaled devices at or near the point-of-care, which will increase the availability of modern medicine to people who do not have ready access to large medical institutions and decrease the cost and delays associated with that lack of access. In this research I present proofs-of-concept for droplet manipulation and droplet-based biochemical analysis using dielectrophoresis as the motive force. Proofs-of-concept developed for the first time in this research include: (1) showing droplet movement on a two-dimensional array of electrodes, (2) achieving controlled dielectric droplet injection, (3) fusing and reacting droplets, and (4) demonstrating a protein fluorescence assay using micro-droplets. ^

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SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^

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Recent data have shown that the percentage of time spent preparing food has decreased during the past few years, and little information is know about how much time people spend grocery shopping. Food that is pre-prepared is often higher in calories and fat compared to foods prepared at home from scratch. It has been suggested that, because of the higher energy and total fat levels, increased consumption of pre-prepared foods compared to home-cooked meals can lead to weight gain, which in turn can lead to obesity. Nevertheless, to date no study has examined this relationship. The purpose of this study is to determine (i) the association between adult body mass index (BMI) and the time spent preparing meals, and (ii) the association between adult BMI and time spent shopping for food. Data on food habits and body size were collected with a self-report survey of ethnically diverse adults between the ages of 17 and 70 at a large university. The survey was used to recruit people to participate in nutrition or appetite studies. Among other data, the survey collected demographic data (gender, race/ethnicity), minutes per week spent in preparing meals and minutes per week spent grocery shopping. Height and weight were self-reported and used to calculate BMI. The study population consisted of 689 subjects, of which 276 were male and 413 were female. The mean age was 23.5 years, with a median age of 21 years. The fraction of subjects with BMI less than 24.9 was 65%, between 25 and 29.9 was 26%, and 30 or greater was 9%. Analysis of variation was used to examine associations between food preparation time and BMI. ^ The results of the study showed that there were no significant statistical association between adult healthy weight, overweight and obesity with either food preparation time and grocery shopping time. Of those in the sample who reported preparing food, the mean food preparation time per week for the healthy weight, overweight, and obese groups were 12.8 minutes, 12.3 minutes, and 11.6 minutes respectively. Similarly, the mean weekly grocery shopping for healthy, overweight, and obese groups were 60.3 minutes per week (8.6min./day), 61.4 minutes (8.8min./day), and 57.3 minutes (8.2min./day), respectively. Since this study was conducted through a University campus, it is assumed that most of the sample was students, and a percentage might have been utilizing meal plans on campus, and thus, would have reported little meal preparation or grocery shopping time. Further research should examine the relationships between meal preparation time and time spent shopping for food in a sample that is more representative of the general public. In addition, most people spent very little time preparing food, and thus, health promotion programs for this population need to focus on strategies for preparing quick meals or eating in restaurants/cafeterias. ^