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- All Subjects: Biochemical markers
- Creators: Dinu, Valentin
- Creators: Shepard, Christina
- Resource Type: Text
Description
Background: Twenty-four hour urinary sucrose and fructose (24uSF) has been developed as a dietary biomarker for total sugars intake. Collection of 24-h urine is associated with high costs and heavy participant burden, while collection of spot urine samples can be easily implemented in research protocols. The aim of this thesis is to investigate the utility of uSF biomarker measured in spot urine. Methods: 15 participants age 22 to 49 years completed a 15-day feeding study in which they consumed their usual diet under controlled conditions, and recorded the time each meal was consumed. Two nonconsecutive 24-hour urines, where each urine void was collected in a separate container, were collected. Four timed voids (morning, afternoon, evening, and next day) were identified based on time of void and meal time. Urine samples were measured for sucrose, fructose and creatinine. Variability of uSF excretion was assessed by coefficient of variation (%CV) and variance ratios. Pearson correlation coefficient and multiple linear regression were used to investigate the association between uSF in each timed void and corresponding 24uSF excretion. Results: The two-day mean uSF was 50.6 mg (SD=29.5) for the 24-h urine, and ranged from 4.5 to 7.5 mg/void for the timed voids. The afternoon void uSF had the lowest within-subject variability (49.1%), and lowest within- to between-subject variance ratio (0.2). The morning and afternoon void uSF had the strongest correlation with 24-h uSF for both mg/void (r=0.80 and r=0.72) and mg/creatinine (r=0.72 and r=0.67), respectively. Finally, the afternoon void uSF along with other covariates had the strongest predictive ability of 24-h uSF excretion (mg/void) (Adjusted R2= 0.69; p=0.002), whereas the morning void had the strongest predictive ability of 24-h uSF excretion (mg/g creatinine) (adjusted R2= 0.58; p=0.008). Conclusions: The afternoon void uSF had the most favorable reproducibility estimates, strong correlation with 24uSF excretion, and explained greatest proportion of the variability in 24uSF. USF in mg/void may be better to use than uSF in mg/g creatinine as a biomarker in spot urine. These findings need to be confirmed in a larger study, and in a study population with a wide range of sugars intake.
ContributorsAverill, Annalisa (Author) / Tasevska, Natasha (Thesis advisor) / Shepard, Christina (Committee member) / Johnston, Carol (Committee member) / Arizona State University (Publisher)
Created2018
Description
Random forest (RF) is a popular and powerful technique nowadays. It can be used for classification, regression and unsupervised clustering. In its original form introduced by Leo Breiman, RF is used as a predictive model to generate predictions for new observations. Recent researches have proposed several methods based on RF for feature selection and for generating prediction intervals. However, they are limited in their applicability and accuracy. In this dissertation, RF is applied to build a predictive model for a complex dataset, and used as the basis for two novel methods for biomarker discovery and generating prediction interval.
Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships.
Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets.
Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships.
Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets.
Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
ContributorsGuan, Xin (Author) / Liu, Li (Thesis advisor) / Runger, George C. (Thesis advisor) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2017
Description
ABSTRACT
Objective: The purpose of this randomized, placebo-controlled trial was to investigate the effect a daily coconut oil supplement (2 grams) would have on a common serum marker of systemic inflammation (C-reactive protein) and an indicator of oxidative stress (TBARS) when compared to the control group receiving a placebo capsule (white flour) in healthy, sedentary adults between the ages of 18-40 in Phoenix, Arizona.
Design: This study was designed as secondary analyses of blood samples originally collected to study the effects of coconut oil supplementation on blood lipids and body composition. The original study consisted of 32 healthy, adult volunteers recruited from the Arizona State University campus in Phoenix, Arizona. Participants followed no food restrictions or special diets, exercised less than 150 minutes per week, had no diagnoses of chronic disease, were not taking statin medications, were non-smokers, and no female participants were pregnant. Participants were randomized into either the Coconut Oil group (CO) or the Placebo group (PL) at week 0, and baseline blood samples and anthropometric measurements were obtained. Each participant completed an 8-week protocol consisting of two supplement capsules daily (coconut oil or placebo). Final fasting blood samples and anthropometric measurements were taken at week 8. This study analyzed the blood samples for measurements of C-reactive protein (CRP) and thiobarbituric reactive substance (TBARS).
Results: Eight weeks of 2 grams per day coconut oil supplementation, in comparison to placebo treatment, did not significantly reduce serum CRP ( -13% and +51% respectively, p=0.183) but did significantly increase TBARS ( +16% and -27% respectively, p=0.049).
Conclusions: Coconut oil supplementation (2 g/day) may impact lipid peroxidation as indicated by an increase in plasma TBARS concentration. Future trials are necessary to corroborate these results using other indices of fatty peroxide formation.
Objective: The purpose of this randomized, placebo-controlled trial was to investigate the effect a daily coconut oil supplement (2 grams) would have on a common serum marker of systemic inflammation (C-reactive protein) and an indicator of oxidative stress (TBARS) when compared to the control group receiving a placebo capsule (white flour) in healthy, sedentary adults between the ages of 18-40 in Phoenix, Arizona.
Design: This study was designed as secondary analyses of blood samples originally collected to study the effects of coconut oil supplementation on blood lipids and body composition. The original study consisted of 32 healthy, adult volunteers recruited from the Arizona State University campus in Phoenix, Arizona. Participants followed no food restrictions or special diets, exercised less than 150 minutes per week, had no diagnoses of chronic disease, were not taking statin medications, were non-smokers, and no female participants were pregnant. Participants were randomized into either the Coconut Oil group (CO) or the Placebo group (PL) at week 0, and baseline blood samples and anthropometric measurements were obtained. Each participant completed an 8-week protocol consisting of two supplement capsules daily (coconut oil or placebo). Final fasting blood samples and anthropometric measurements were taken at week 8. This study analyzed the blood samples for measurements of C-reactive protein (CRP) and thiobarbituric reactive substance (TBARS).
Results: Eight weeks of 2 grams per day coconut oil supplementation, in comparison to placebo treatment, did not significantly reduce serum CRP ( -13% and +51% respectively, p=0.183) but did significantly increase TBARS ( +16% and -27% respectively, p=0.049).
Conclusions: Coconut oil supplementation (2 g/day) may impact lipid peroxidation as indicated by an increase in plasma TBARS concentration. Future trials are necessary to corroborate these results using other indices of fatty peroxide formation.
ContributorsNorman, Lisa (Author) / Johnston, Carol (Thesis advisor) / Shepard, Christina (Committee member) / Ellis, Melissa (Committee member) / Arizona State University (Publisher)
Created2017
Description
Breast cancer is the most common cancer and currently the second leading cause of death among women in the United States. Patients’ five-year relative survival rate decreases from 99% to 25% when breast cancer is diagnosed late. Immune checkpoint blockage has shown to be a promising therapy to improve patients’ outcome in many other cancers. However, due to the lack of early diagnosis, the treatment is normally given in the later stages. An early diagnosis system for breast cancer could potentially revolutionize current treatment strategies, improve patients’ outcomes and even eradicate the disease. The current breast cancer diagnostic methods cannot meet this demand. A simple, effective, noninvasive and inexpensive early diagnostic technology is needed. Immunosignature technology leverages the power of the immune system to find cancer early. Antibodies targeting tumor antigens in the blood are probed on a high-throughput random peptide array and generate a specific binding pattern called the immunosignature.
In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.
Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.
In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.
Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.
ContributorsDuan, Hu (Author) / Johnston, Stephen Albert (Thesis advisor) / Hartwell, Leland Harrison (Committee member) / Dinu, Valentin (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015