University of North Carolina at Greensboro

About: University of North Carolina at Greensboro is a education organization based out in Greensboro, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 5481 authors who have published 13715 publications receiving 456239 citations. The organization is also known as: UNCG & UNC Greensboro.

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning

Abstract: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients. Our research explores data-driven approaches which utilize supervised machine learning models to identify patients with such diseases. Using the National Health and Nutrition Examination Survey (NHANES) dataset, we conduct an exhaustive search of all available feature variables within the data to develop models for cardiovascular, prediabetes, and diabetes detection. Using different time-frames and feature sets for the data (based on laboratory data), multiple machine learning models (logistic regression, support vector machines, random forest, and gradient boosting) were evaluated on their classification performance. The models were then combined to develop a weighted ensemble model, capable of leveraging the performance of the disparate models to improve detection accuracy. Information gain of tree-based models was used to identify the key variables within the patient data that contributed to the detection of at-risk patients in each of the diseases classes by the data-learned models. The developed ensemble model for cardiovascular disease (based on 131 variables) achieved an Area Under - Receiver Operating Characteristics (AU-ROC) score of 83.1% using no laboratory results, and 83.9% accuracy with laboratory results. In diabetes classification (based on 123 variables), eXtreme Gradient Boost (XGBoost) model achieved an AU-ROC score of 86.2% (without laboratory data) and 95.7% (with laboratory data). For pre-diabetic patients, the ensemble model had the top AU-ROC score of 73.7% (without laboratory data), and for laboratory based data XGBoost performed the best at 84.4%. Top five predictors in diabetes patients were 1) waist size, 2) age, 3) self-reported weight, 4) leg length, and 5) sodium intake. For cardiovascular diseases the models identified 1) age, 2) systolic blood pressure, 3) self-reported weight, 4) occurrence of chest pain, and 5) diastolic blood pressure as key contributors. We conclude machine learned models based on survey questionnaire can provide an automated identification mechanism for patients at risk of diabetes and cardiovascular diseases. We also identify key contributors to the prediction, which can be further explored for their implications on electronic health records.

Training monitoring skills improves older adults' self-paced associative learning.

Abstract: We investigated a memory-enhancement program that involved teaching older adults to regulate study through self-testing A regulation group was taught standard strategies along with self-testing techniques for identifying less well-learned items that could benefit from extra study This group was compared with a strategy-control group, which was taught only strategies, and with a waiting-list control group Greater training gains were shown for the regulation group (effect size, d = 072) than for the strategy-control (d = 028) and waiting-list control (d = 003) groups, indicating that training a monitoring skill--self-testing--can improve older adults' learning

Synergy-directed fractionation of botanical medicines: a case study with goldenseal (Hydrastis canadensis).

Abstract: It is often argued that the efficacy of herbal medicines is a result of the combined action of multiple constituents that work synergistically or additively. Determining the bioactive constituents in these mixtures poses a significant challenge. We have developed an approach to address this challenge, synergy-directed fractionation, which combines comprehensive mass spectrometry profiling with synergy assays and natural products isolation. The applicability of synergy-directed fractionation was demonstrated using the botanical medicine goldenseal (Hydrastis canadensis) as a case study. Three synergists from goldenseal were identified, sideroxylin, 8-desmethyl-sideroxylin, and 6-desmethyl-sideroxylin. These flavonoids synergistically enhance the antimicrobial activity of the alkaloid berberine (also a constituent of H. canadensis) against Staphylococcus aureus by inhibition of the NorA multidrug resistance pump. The flavonoids possess no inherent antimicrobial activity against S. aureus; therefore, they could have been missed using traditional bioactivity-directed fractionation. The flavonoid synergists are present at higher concentration in extracts from H. canadensis leaves, while the antimicrobial alkaloid berberine is present at higher levels in H. canadensis roots. Thus, it may be possible to produce an extract with optimal activity against S. aureus using a combination of goldenseal roots and leaves.

The facets of group technology and their impacts on implementation—A state-of-the-art survey

Abstract: The recent use of the Group Technology concept of grouping parts into ‘families’ has received much notice due to the integration of this technique with the varied technologies of computer integrated manufacturing. This is but one of the facets of Group Technology, which is essentially a set of techniques and operating policies designed to improve the operational efficiency of manufacturing. With the exception of the parts grouping concept, the many facets of Group Technology have been largely neglected. This paper presents a survey of the literature on the concepts of Group Technology, attempts to categorize these topics in a natural manner, and link the processes, technology and experiences together. This paper reviews the problems addressed and the optimal and heuristic solution methodologies suggested in the literature. The conclusion indicate various areas and topics for future research/implementation activities.

A distinct metabolic signature of human colorectal cancer with prognostic potential

Abstract: Purpose: Metabolic phenotyping has provided important biomarker findings, which, unfortunately, are rarely replicated across different sample sets due to the variations from different analytical and clinical protocols used in the studies. To date, very few metabolic hallmarks in a given cancer type have been confirmed and validated by use of a metabolomic approach and other clinical modalities. Here, we report a metabolomics study to identify potential metabolite biomarkers of colorectal cancer with potential theranostic value. Experimental Design: Gas chromatography–time-of-flight mass spectrometry (GC–TOFMS)–based metabolomics was used to analyze 376 surgical specimens, which were collected from four independent cohorts of patients with colorectal cancer at three hospitals located in China and City of Hope Comprehensive Cancer Center in the United States. Differential metabolites were identified and evaluated as potential prognostic markers. A targeted transcriptomic analysis of 29 colorectal cancer and 27 adjacent nontumor tissues was applied to analyze the gene expression levels for key enzymes associated with these shared metabolites. Results: A panel of 15 significantly altered metabolites was identified, which demonstrates the ability to predict the rate of recurrence and survival for patients after surgery and chemotherapy. The targeted transcriptomic analysis suggests that the differential expression of these metabolites is due to robust metabolic adaptations in cancer cells to increased oxidative stress as well as demand for energy, and macromolecular substrates for cell growth and proliferation. Conclusions: These patients with colorectal cancer, despite their varied genetic background, mutations, pathologic stages, and geographic locations, shared a metabolic signature that is of great prognostic and therapeutic potential. Clin Cancer Res; 20(8); 2136–46. ©2014 AACR .