ABSTRACT Assessment of the Effects of Malaria and Anemia in Pregnant Malawian Women Before and After Treatment of Moderate Malnutrition William Shipley Background: Moderate acute malnutrition (MAM) can lead to adverse maternal and infant outcomes and possibly further complications. Supplementary foods or treatments with high quality nutrients should be administered to those with MAM in hopes to increase the chance of healthy maternal and infant outcomes. Sometimes supplementary food is not enough to overcome MAM and disease may play a role, particularly in pregnant malnourished women. Objective: To determine if the effects of malaria and anemia moderated the effect of nutritional treatments (one of the three given nutritional interventions) used to improve malnutrition and achieve a MUAC ≥ 23 cm during study participation. Additionally, this research serves to assess whether the relationship between malaria and anemia is associated with malnutrition status. Methods: Women were given a dose of IPTp at each antenatal visit between zero and four total IPTp doses. Infant anthropometrics – length, weight, head circumference, and MUAC were taken at birth, 6 weeks, and 3 months. Maternal hemoglobin levels were assessed at enrollment and after 10 weeks of enrollment as well as infant hemoglobin at 3 months. Anemia was defined by a hemoglobin less than 11.0 g/dL. Mild anemia was defined as hemoglobin greater than 7.0 but less than 9.9 g/dL and moderate anemia was defined by hemoglobin values 9.9 or greater but less than 11.0 g/dL. Analysis was completed using ANOVA, and if any significant differences were observed, they were compared via Tukey HSD (continuous) or Chi-squared test (categorical). Results: Total number of IPTp doses was found to be a more statistically significant predictor of maternal weight gain during treatment than timing of the doses. It stands to reason that women receiving three or more IPTp doses was the most beneficial for women during treatment as it saw the highest increases in maternal weight gain. At baseline, women that achieved a MUAC > 23 cm during the study was 32.0% (n = 1805). The greatest proportion of women, after adjustment, that achieved a MUAC ≥ 23 cm was seen in women receiving four (47.3 %) and three (37.8 %) total IPTp doses during pregnancy. Maternal weight gain correlated closely with hemoglobin at enrollment (p-value = 0.0111). Total number of IPTp doses received during pregnancy was not found to have a statistical effect on infant hemoglobin or anemia at three months. Infant length at six weeks was higher in infants from mothers that received two or three IPTp doses compared to mothers that received one IPTp dose (p-value = 0.0218). A p-value below 0.05 by total number of IPTp doses was observed for infant weight, head circumference, and MUAC at birth, six weeks, and three months. Conclusion: At least three IPTp was effective in improving maternal weight gain and achievement of MUAC > 23 cm as well as improved many infant outcomes. Hemoglobin at enrollment was a predictor of maternal weight gain during tx but was not associated with any other outcomes.

<p> A study was made to determine the elimination rate of soluble organic and inorganic compounds during anaerobic storage. Using aerobic storage as a comparison, some insight could then be gained into the metabolic activities of a mixed culture.</p> <p> The rate of oxygen utilization following anaerobic storage appeared to be dependent on the level of aeration within the reactor prior to storage. There was no liberation of carbon to account for the increased oxygen uptake following storage. Although there was a slight decrease in the solids level, it was independent of the type of storage.</p> <p> The nitrate ion appeared to act as a hydrogen acceptor during anaerobic storage and, upon reaeration, the nitrate level tended to level off. There was a production of ammonia during both aerobic and anaerobic storage indicating a deamination of cellular material (loss of solids).</p> / Thesis / Master of Engineering (MEngr)

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Non-nutritive sweeteners (NNS) have been used to replace added sugars in foods/beverages. Research related to NNS consumption and metabolic syndrome (MetS) is of great importance as NNS are often used by individuals who are looking to improve their health. The objectives of this investigation were to determine whether an association between NNS consumption (total and individual types) and MetS exists, and if any of the five risk factors for MetS were more significantly impacted by NNS consumption. Four NNS were included in this study: saccharin, sucralose, aspartame, and acesulfame potassium. Adult participants (n = 125) from Southwest Virginia were recruited for a cross-sectional investigation. Demographics, three 24-hour dietary recalls, and values for MetS (blood pressure, waist circumference, and glucose, triglyceride, and HDL levels) were collected. Statistical analyses included descriptives and multiple linear regressions models. Of the 125 participants, 63 were classified as NNS consumers and 18 met the criteria for MetS. There was a significant positive relationship between MetS and total NNS consumption (p=0.007) and MetS and aspartame (p=0.012). When looking at individual MetS risk factors, waist circumference, triglyceride and glucose values were significantly positively associated with NNS consumption (p≤0.001) and aspartame, sucralose, and saccharin (all p≤0.027). Some limitations to current NNS research were addressed, such as, examining associations between individual NNS types and not using diet soda as a proxy for NNS consumption. More research is needed to address the bias of self-reported data and the lack of randomized controlled trials to inferentially test the impact of NNS consumption. / Master of Science

Second-generation antipsychotics (SGAs) are prescribed for treatment of psychosis. A major side effect of SGAs is an increased risk of metabolic syndrome (MetS) with symptoms of hypertension, hyperlipidemia, hyperglycemia, and truncal obesity. A clinic in the northeastern United States was not screening patients for MetS when being treated with SGAs. The purpose of this project was to educate staff on MetS risk factors, signs, symptoms, and patient management with a goal to improve their knowledge of MetS. Lewin's change theory provided a conceptual framework for the project. The project question explored the development and evaluation of an educational module on MetS increased staff knowledge. Educational content was guided by current literature and the American Psychiatric Association and American Diabetic Association practice guidelines. Five expert panel members, consisting of 3 psychiatrists, an advance practice nurse, and a registered nurse reviewed the education program and evaluated content using a Likert-type questionnaire. Expert panel evaluations indicated that the module content contained useful clinical information on MetS screening for patients on SGAs. After panel review, the program was presented to 7 clinic staff. Pretest and posttest questionnaires asked 10 multiple choice questions and results were compared. Questions on SGA side effects, MetS complications, prevalence, baseline assessment measures, lab work, and needed collaboration were answered correctly by 6 of the participants pretest and all questions after receiving the education program. The project has the potential to promote positive social change through staff education on MetS screening for patients, thus improving patient outcomes.

This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the form of metabolic pathways. A metabolic pathway describes a set of chemical reactions that perform a specific biological function. A significant amount of biological knowledge produced by efforts to identify and understand these pathways is formalized in readily accessible databases such as the Kyoto Encyclopedia of Genes and Genomes. PIA uses metabolic pathways to identify relationships among the metabolite concentrations that are measured by a metabolic profile. Specifically, PIA assumes that the class-conditional metabolite concentrations (diseased vs. healthy, respectively) follow multivariate normal distributions. It further assumes that conditional independence statements about these distributions derived from the pathways relate the concentrations of the metabolites to each other. The two assumptions allow for a natural representation of the class-conditional distributions using a type of probabilistic graphical model called a Gaussian Markov Random Field. PIA efficiently estimates the parameters defining these distributions from example patients to produce a classifier. It classifies an undiagnosed patient by evaluating both models to determine the most probable class given their metabolic profile. We apply PIA to a data set of cancer patients to diagnose those with a muscle wasting disease called cachexia. Standard machine learning algorithms such as Naive Bayes, Tree-augmented Naive Bayes, Support Vector Machines and C4.5 are used to evaluate the performance of PIA. The overall classification accuracy of PIA is better than these algorithms on this data set but the difference is not statistically significant. We also apply PIA to several other classification tasks. Some involve predicting various manipulations of the metabolic processes performed in experiments with worms. Other tasks are to classify pigs according to properties of their dietary intake. The accuracy of PIA at these tasks is not significantly better than the standard algorithms.

The field of biochemical systems modeling and analysis is faced with an unprecedented flood of data from experimental methodologies of molecular biology. While these techniques continue to leapfrog ahead in the speed, volume and finesse with which they generate data, the methods of data analysis and interpretation, however, are still playing the catch-up game. The notions of systems analysis have found a new foothold, under the banner of Systems Biology, with the promise of uncovering the rationale for the designs of biological systems from their parts lists, as they are generated by experimentation and sorted and managed by bioinformatics tools. With an aim to complement hypothesis-driven and reductionistic biological research, and not replace it, a systems biologist relies on the tools of mathematical and computational modeling to be able to contribute meaningfully to any ongoing bio-molecular systems research. These systems analysis tools, however, should not only have their roots steeped well in the theoretical foundations of biochemistry, mathematics and numerical computation, but they should be married to a framework that facilitates the required systems way of thought for all its users computational scientists, experimentalists and molecular biologists alike. Hopefully, such framework-based tools would go beyond just providing fancy GUIs, numerical packages for integrating ODEs and/or optimization libraries. The intent of this thesis is to present a framework and toolbox for biochemical systems modeling, with an application in metabolic pathway analysis and/or metabolic engineering. The research presented here builds upon the tenets of a very well established and generic approach to biological systems modeling and analysis, called Biochemical Systems Theory (BST), which is almost forty years old. The nuances of modeling and practical hurdles to analysis are presented in the context of a real-time case study of analyzing the glucolytic pathway in the bacterium Lactococcus lactis. Alongside, the thesis presents the features of a MATLAB-based software application that has been built upon the framework of BST and is aptly named as Biochemical Systems Toolbox (BSTBox). The thesis presents novel contributions, made by the author during the course of his research, to state-of-the-art techniques in parameter estimation, and robustness and sensitivity analysis topics that, as this thesis will show, remain to be the most restrictive bottlenecks in the world of biological systems modeling and analysis.

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Thesis (Ph. D.)--Michigan State University. Human Nutrition, 2008. / Title from PDF t.p. (Proquest, viewed on Aug. 24, 2009) Includes bibliographical references (p. 141-163). Also issued in print.

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