scholarly journals The National Children's Study Archive Model: A 3-Tier Framework for Dissemination of Data and Specimens for General Use and Secondary Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Peter K. Gilbertson ◽  
Susan Forrester ◽  
Linda Andrews ◽  
Kathleen McCann ◽  
Lydia Rogers ◽  
...  
Keyword(s):  
Secondary Analysis ◽  
Data Access ◽  
Data Exploration ◽  
Level Data ◽  
Approaches To Study ◽  
Health And Development ◽  
Visualization Tools ◽  
Access Model ◽  
Pregnancy And Birth ◽  
Request Management

The National Children's Study (NCS) Archive was created as a repository of samples, data, and information from the NCS Vanguard Study—a longitudinal pregnancy and birth cohort evaluating approaches to study influence of environmental exposures on child health and development—to provide qualified researchers with access to NCS materials for use in secondary research. The National Children's Study Archive (NCSA) model is a 3-tiered access model designed to make the wealth of information and materials gathered during the NCS Vanguard Study available at a user appropriate level. The NCSA model was developed as a 3-tier framework, for users of varying access levels, providing intuitive data exploration and visualization tools, an end-to-end data and sample request management system, and a restricted portal for participant-level data access with a team of experts available to assist users. This platform provides a model to accelerate transformation of information and materials from existing studies into new scientific discoveries.Trial Registration:ClinicalTrials.gov Identifier: NCT00852904 (first posted February 27, 2009).

scholarly journals Towards a Transparent Data Access Model for the GridRPC Paradigm

2007 ◽  
pp. 269-284 ◽  
Author(s):  
Gabriel Antoniu ◽  
Eddy Caron ◽  
Frédéric Desprez ◽  
Aurélia Fèvre ◽  
Mathieu Jan
Keyword(s):  
Data Access ◽  
Access Model

scholarly journals Cohort Profile: The China Multi-Ethnic Cohort (CMEC) Study

2020 ◽  
Author(s):  
Xing Zhao ◽  
Feng Hong ◽  
Jianzhong Yin ◽  
Wenge Tang ◽  
Gang Zhang ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Disease Incidence ◽  
Data Access ◽  
Population Based ◽  
Baseline Survey ◽  
Individual Level ◽  
Long Term Storage ◽  
Level Data ◽  
The Individual ◽  
Study Participants

AbstractCohort purposeThe China Multi-Ethnic Cohort (CMEC) is a community population-based prospective observational study aiming to address the urgent need for understanding NCD prevalence, risk factors and associated conditions in resource-constrained settings for ethnic minorities in China.Cohort BasicsA total of 99 556 participants aged 30 to 79 years (Tibetan populations include those aged 18 to 30 years) from the Tibetan, Yi, Miao, Bai, Bouyei, and Dong ethnic groups in Southwest China were recruited between May 2018 and September 2019.Follow-up and attritionAll surviving study participants will be invited for re-interviews every 3-5 years with concise questionnaires to review risk exposures and disease incidence. Furthermore, the vital status of study participants will be followed up through linkage with established electronic disease registries annually.Design and MeasuresThe CMEC baseline survey collected data with an electronic questionnaire and face-to-face interviews, medical examinations and clinical laboratory tests. Furthermore, we collected biological specimens, including blood, saliva and stool, for long-term storage. In addition to the individual level data, we also collected regional level data for each investigation site.Collaboration and data accessCollaborations are welcome. Please send specific ideas to corresponding author at: [email protected].

scholarly journals 1539Australia’s worsening mental health – what’s next?

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Shrinkhala Dawadi ◽  
Frances Shawyer ◽  
Helena Teede ◽  
Graham Meadows ◽  
Joanne Enticott
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Secondary Analysis ◽  
Symptom Burden ◽  
Population Level ◽  
Policy And Strategy ◽  
Level Data ◽  
Related Symptom ◽  
Australian Census ◽  
Population Mental Health

Abstract Background The population prevalence of mental illness over time, and by sociodemographic subgroups, are important benchmark data. Examining reliable population level data can highlight groups with greater mental-illness related symptom burden and inform policy and strategy. Methods Secondary analysis of Australian National Health Surveys (n = 78,204) from 2001-02 to 2017-18. Trends in the prevalence of very high scores on the Kessler-10 (K10), a measure of psychological distress capturing symptoms of affective and anxiety disorders, were examined by time, age, gender, and socioeconomic status. Data were standardised to the 2001 Australian census population on the strata of sex and age. Results In 2017-18, the rate of probable mental illness was estimated at 5.1%, a 1.5% increase (representing an additional 367,000 Australians) since 2007. In 2017-18, the subgroups with the highest rates were women aged 18-24 (8.01%, 95% CI = 5.9%-10.2%), and the poorest fifth of Australians (8.02%, 95% CI = 7.0%-9.0%). Women aged 55-64 demonstrated the greatest increase in rates (2001: 3.5%, 95% CI = 2.5%-4.6%; 2017: 7.2%, 95% CI = 5.9%-8.5%; z = 4.10, p ≤ 0.001). Conclusions Despite efforts to improve population mental health, rates of probable mental illness in Australia have increased since 2007. Findings will be discussed in conjunction to extant social and health policies, and potential gaps in the delivery of gold-standard mental health care. Key messages The rate of probable mental illness in Australia seem to be increasing, especially in women aged 55-64, and those from low-SES backgrounds.

scholarly journals 2020 Jeffrey M. Hoeg Award Lecture

2020 ◽  
Author(s):  
Elena Aikawa ◽  
Mark C. Blaser
Keyword(s):  
Heart Valves ◽  
Molecular Level ◽  
Clinical Problem ◽  
Network Medicine ◽  
The Past ◽  
Ectopic Tissue ◽  
Level Data ◽  
Approaches To Study ◽  
Vascular Beds ◽  
Mechanistic Basis

Cardiovascular calcification is an insidious form of ectopic tissue mineralization that presents as a frequent comorbidity of atherosclerosis, aortic valve stenosis, diabetes, renal failure, and chronic inflammation. Calcification of the vasculature and heart valves contributes to mortality in these diseases. An inability to clinically image or detect early microcalcification coupled with an utter lack of pharmaceutical therapies capable of inhibiting or regressing entrenched and detectable macrocalcification has led to a prominent and deadly gap in care for a growing portion of our rapidly aging population. Recognition of this mounting concern has arisen over the past decade and led to a series of revolutionary works that has begun to pull back the curtain on the pathogenesis, mechanistic basis, and causative drivers of cardiovascular calcification. Central to this progress is the discovery that calcifying extracellular vesicles act as active precursors of cardiovascular microcalcification in diverse vascular beds. More recently, the omics revolution has resulted in the collection and quantification of vast amounts of molecular-level data. As the field has become poised to leverage these resources for drug discovery, new means of deriving relevant biological insights from these rich and complex datasets have come into focus through the careful application of systems biology and network medicine approaches. As we look onward toward the next decade, we envision a growing need to standardize approaches to study this complex and multifaceted clinical problem and expect that a push to translate mechanistic findings into therapeutics will begin to finally provide relief for those impacted by this disease.

scholarly journals Visualizing Structure and Transitions for Biological Data Exploration

2017 ◽  
Author(s):  
Kevin R. Moon ◽  
David van Dijk ◽  
Zheng Wang ◽  
Scott Gigante ◽  
Daniel B. Burkhardt ◽  
...  
Keyword(s):  
Biological Data ◽  
Data Exploration ◽  
High Dimensional ◽  
Visualization Method ◽  
Mass Cytometry ◽  
Nonlinear Structure ◽  
Single Cell Rna Sequencing ◽  
Visualization Tools ◽  
Microbiome Data ◽  
Germ Layer Differentiation

AbstractWith the advent of high-throughput technologies measuring high-dimensional biological data, there is a pressing need for visualization tools that reveal the structure and emergent patterns of data in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure in data by an information-geometric distance between datapoints. We perform extensive comparison between PHATE and other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data including continual progressions, branches, and clusters. We define a manifold preservation metric DEMaP to show that PHATE produces quantitatively better denoised embeddings than existing visualization methods. We show that PHATE is able to gain unique insight from a newly generated scRNA-seq dataset of human germ layer differentiation. Here, PHATE reveals a dynamic picture of the main developmental branches in unparalleled detail, including the identification of three novel subpopulations. Finally, we show that PHATE is applicable to a wide variety of datatypes including mass cytometry, single-cell RNA-sequencing, Hi-C, and gut microbiome data, where it can generate interpretable insights into the underlying systems.

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