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RISS 인기검색어
- 검색키워드
- 저자 : Inocencio, Timothy J (검색결과 2 건)
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Inocencio, Timothy J Virginia Commonwealth University 2012 해외박사(DDOD)
<bold>Introduction:</bold> Opioid poisoning has been rapidly increasing in the past decade, and has been driven in large part due to increases in opioid prescribing. This has been accompanied by intervention efforts aimed at preventing and reversing opioid poisoning through naloxone prescription programs. Current literature have not quantified the economic burden of opioid poisoning. Understanding this information can help inform these efforts and bring light to this growing problem. In addition understanding various determinants of increased costs can help to identify the types of populations more likely to have greater costs. <bold>Main Objectives:</bold> The objectives are 1) to quantify the economic burden of opioid poisoning, 2) to evaluate differences in costs, LOS, and in-hospital mortality depending on opioid type, 3) to identify opioids most likely to result in hospitalization for opioid-related ED visits and 4) to determine differences in the odds of admission to various hospital admission categories with respect to opioid type. <bold>Methods:</bold> A cost-of-illness approach was used to estimate the economic burden of opioid poisoning. Direct costs and prevalence estimates were obtained from nationally representative databases. Other sources of direct costs were obtained from the literature. Indirect costs were measured using the human capital method. Differences in costs, LOS, and in-hospital mortality were measured through generalized linear models using the National Inpatient Sample in 2009 from the Healthcare Cost and Utilization Project. The Drug Abuse Warning Network database was used to evaluate opioids most likely to result in hospitalization and to evaluate the likelihood of different opioids to cause admission into different types of hospital settings. <bold>Results:</bold> Opioid poisoning resulted in an economic burden approximately $20.4 billion dollars in 2009. Productivity losses were associated with 89% of this total. Direct medical costs were associated with $2.2 billion. Methadone was associated with the greatest inpatient costs and LOS, while heroin was associated with a greater likelihood of in-patient mortality compared to prescription opioids. Heroin, methadone, and morphine were associated with the greatest odds of hospitalization. Among admitted patients, methadone, morphine, and fentanyl were each associated with the greatest odds of ICU admission compared with other opioids. <bold>Conclusions:</bold> Opioid poisoning results in a significant economic burden to society. Costs, length of stay, in-patient mortality and the odds of hospitalization and admission type depend on the type of opioid involved. The results from this study can be used to inform policy efforts in providing interventions to reduce opioid poisoning and help focus efforts on populations at highest risk for increased costs.
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Nepomuceno, Angelito Inocencio North Carolina State University ProQuest Dissertat 2014 해외박사(DDOD)
In the era of "-omics" based technologies, mass spectrometry (MS) has been synonymous with proteomics. This has only been accomplished with the advent of electrospray ionization and matrix-assisted laser desorption ionization techniques, whom Dr. John B. Fenn and Dr. Koichi Tanaka received the Nobel Prize in chemistry in 2004. A variety of proteomics based mass spectrometric workflows are utilized to define an organism's proteome. Each workflow is dependent on many necessary tools. First, a reference database comprised of proteins, expressed sequence tags and/or a genome sequence database. Secondly, analytical separation techniques are necessary for complex mixtures. A wide variety of techniques can be used to separate complex mixtures, i.e. SDS-PAGE, HPLC, Filter Aided Sample Preparation, and Stage-tip fractionation. To analyze these complex mixtures, mass spectrometers that can provide the necessary sensitivity, robustness and accuracy are necessary. Lastly, the identification of proteins is then feasible with a series of software suites that match the MS data to that of the protein sequences in the provided database. Herein, this dissertation describes several approaches used to define global proteomes of tissue samples obtained from model biological systems. Animal models are essential towards advancements in basic and clinical research. The domestic hen (Gallus gallus) was used as an animal model to investigate ovarian cancer and basic reproductive studies. There have been mammalian models which have been used to investigate ovarian cancer. However, scientists have not found a suitable animal model that spontaneously develops ovarian cancer. The chicken on the other hand not only develops ovarian cancer spontaneously, the types of cancers found are similar to those that occur in humans. The ovaries of egg laying hens were also used to study follicular development and recruitment. Small white follicles were excised from the cortex of the ovary in order to obtain global proteome profiles of the white yolk, follicular wall and stromal cells. The proteomes provided were used to investigate follicular recruitment. Proteins extracted from tissue samples were processed in several methods prior to analysis via LC-MS/MS. Protein samples obtained from the ovarian cancer study were processed on a SDS-PAGE. Gel lanes were fractionated in order to maximize the proteome coverage. Samples were then analyzed using a nanoLC coupled to a LTQ-FTICR mass spectrometer. In a short number of years, many advancement in techniques used for sample preparation were conceived. Limited by the amount of samples obtained using laser microdissection experiments; a filter aided sample preparation (FASP) protocol was used. To further increase proteome coverage, samples were analyzed on a linear quadrupole orbitrap mass analyzer due to the increase in sensitivity, mass accuracy of both MS and MS/MS spectra, and speed of analysis.
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