Dataset Information

Understanding Atherosclerosis Through an Osteoarthritis Data Set.

ABSTRACT: Atherosclerotic cardiovascular disease remains a worldwide epidemic and one of the leading causes of death nowadays. Vessel wall imaging can be used to understand the development and progression of atherosclerosis, but it is rarely done because of the high cost. We recently identified the Osteoarthritis Initiative, a large prospective cohort study of knee osteoarthritis, which might serve as a valuable source for atherosclerosis research with its serial knee magnetic resonance imaging data. We have found that these images are suitable for vessel wall image analysis of the lower extremity arteries. Here, we will introduce the Osteoarthritis Initiative data set and explain why it could be used for cardiovascular research purposes. Also, we will briefly comment on peripheral artery atherosclerosis as it is covered in the Osteoarthritis Initiative image data set and review the use of vessel wall imaging for studying atherosclerosis. We think data mining of imaging studies, not originally designed on cardiovascular research, can not only maximize the value of the imaging data set but also boost our understanding of atherosclerosis.

SUBMITTER: Liu W

PROVIDER: S-EPMC7886009 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Understanding Atherosclerosis Through an Osteoarthritis Data Set.

Liu Wenjin W Balu Niranjan N Canton Gador G Hippe Daniel S DS Watase Hiroko H Waterton John C JC Hatsukami Thomas T Yuan Chun C

Arteriosclerosis, thrombosis, and vascular biology 20190601 6

Atherosclerotic cardiovascular disease remains a worldwide epidemic and one of the leading causes of death nowadays. Vessel wall imaging can be used to understand the development and progression of atherosclerosis, but it is rarely done because of the high cost. We recently identified the Osteoarthritis Initiative, a large prospective cohort study of knee osteoarthritis, which might serve as a valuable source for atherosclerosis research with its serial knee magnetic resonance imaging data. We h ...[more]

PMID: 31070477

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Project description:BackgroundToxoplasma gondii infection causes substantial morbidity and mortality in the United States, and infects approximately one-third of persons globally. Clinical manifestations vary. Seropositivity is associated with neurologic diseases and malignancies. There are few objective data concerning US incidence and distribution of toxoplasmosis.MethodsTruven Health MarketScan Database and International Classification of Diseases, Ninth Revision (ICD-9) codes, including treatment specific to toxoplasmosis, identified patients with this disease. Spatiotemporal distribution and patterns of disease manifestation were analyzed. Comorbidities between patients and matched controls were compared.ResultsBetween 2003 and 2012, 9260 patients had ICD-9 codes for toxoplasmosis. This database of patients with ICD-9 codes includes 15% of those in the United States, excluding patients with no or public insurance. Thus, assuming that demographics do not change incidence, the calculated total is 61 700 or 6856 patients per year. Disease was more prevalent in the South. Mean age at diagnosis was 37.5 ± 15.5 years; 2.4% were children aged 0-2 years, likely congenitally infected. Forty-one percent were male, and 73% of women were of reproductive age. Of identified patients, 38% had eye disease and 12% presented with other serious manifestations, including central nervous system and visceral organ damage. Toxoplasmosis was statistically associated with substantial comorbidities, including human immunodeficiency virus, autoimmune diseases, and neurologic diseases.ConclusionsToxoplasmosis causes morbidity and mortality in the United States. Our analysis of private insurance records missed certain at-risk populations and revealed fewer cases of retinal disease than previously estimated, suggesting undercoding, underreporting, undertreating, or differing demographics of those with eye disease. Mandatory reporting of infection to health departments and gestational screening could improve care and facilitate detection of epidemics and, thereby, public health interventions.

Project description:We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges.

Dataset Information

Understanding Atherosclerosis Through an Osteoarthritis Data Set.

Publications

Understanding Atherosclerosis Through an Osteoarthritis Data Set.

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