scholarly journals Streamlining statistical reproducibility: NHLBI ORCHID clinical trial results reproduction

JAMIA Open ◽  
2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Arnaud Serret-Larmande ◽  
Jonathan R Kaltman ◽  
Paul Avillach
Keyword(s):  
Clinical Trial ◽  
Peer Review Process ◽  
Application Programming Interface ◽  
Data Repository ◽  
Practical Implementation ◽  
Media Audiences ◽  
Blood Institute ◽  
National Heart Lung ◽  
Application Programming ◽  
Clinical Trial Results

Abstract Reproducibility in medical research has been a long-standing issue. More recently, the COVID-19 pandemic has publicly underlined this fact as the retraction of several studies reached out to general media audiences. A significant number of these retractions occurred after in-depth scrutiny of the methodology and results by the scientific community. Consequently, these retractions have undermined confidence in the peer-review process, which is not considered sufficiently reliable to generate trust in the published results. This partly stems from opacity in published results, the practical implementation of the statistical analysis often remaining undisclosed. We present a workflow that uses a combination of informatics tools to foster statistical reproducibility: an open-source programming language, Jupyter Notebook, cloud-based data repository, and an application programming interface can streamline an analysis and help to kick-start new analyses. We illustrate this principle by (1) reproducing the results of the ORCHID clinical trial, which evaluated the efficacy of hydroxychloroquine in COVID-19 patients, and (2) expanding on the analyses conducted in the original trial by investigating the association of premedication with biological laboratory results. Such workflows will be encouraged for future publications from National Heart, Lung, and Blood Institute-funded studies.

scholarly journals NASA GeneLab: interfaces for the exploration of space omics data

2020 ◽  
Vol 49 (D1) ◽  
pp. D1515-D1522 ◽  
Author(s):  
Daniel C Berrios ◽  
Jonathan Galazka ◽  
Kirill Grigorev ◽  
Samrawit Gebre ◽  
Sylvain V Costes
Keyword(s):  
Data Entry ◽  
Application Programming Interface ◽  
Data Repository ◽  
Omics Data ◽  
Online Data ◽  
Support Tools ◽  
Working Groups ◽  
Student Training ◽  
Application Programming ◽  
Space Environments

Abstract The mission of NASA’s GeneLab database (https://genelab.nasa.gov/) is to collect, curate, and provide access to the genomic, transcriptomic, proteomic and metabolomic (so-called ‘omics’) data from biospecimens flown in space or exposed to simulated space stressors, maximizing their utilization. This large collection of data enables the exploration of molecular network responses to space environments using a systems biology approach. We review here the various components of the GeneLab platform, including the new data repository web interface, and the GeneLab Online Data Entry (GEODE) web portal, which will support the expansion of the database in the future to include companion non-omics assay data. We discuss our design for GEODE, particularly how it promotes investigators providing more accurate metadata, reducing the curation effort required of GeneLab staff. We also introduce here a new GeneLab Application Programming Interface (API) specifically designed to support tools for the visualization of processed omics data. We review the outreach efforts by GeneLab to utilize the spaceflight data in the repository to generate novel discoveries and develop new hypotheses, including spearheading data analysis working groups, and a high school student training program. All these efforts are aimed ultimately at supporting precision risk management for human space exploration.

scholarly journals Use of the National Heart, Lung, and Blood Institute Data Repository

2017 ◽  
Vol 376 (19) ◽  
pp. 1849-1858 ◽  
Author(s):  
Sean A. Coady ◽  
George A. Mensah ◽  
Elizabeth L. Wagner ◽  
Miriam E. Goldfarb ◽  
Denise M. Hitchcock ◽  
...  
Keyword(s):  
Data Repository ◽  
Blood Institute ◽  
National Heart ◽  
National Heart Lung

scholarly journals IoT PULSE OXIMETRY STATUS MONITORING FOR HOME QUARANTINED COVID-19 PATIENTS

2020 ◽  
Vol 20 (4) ◽  
pp. 1029-1036
Author(s):  
VIOREL MIRON-ALEXE
Keyword(s):  
Pulse Oximetry ◽  
Infected Patient ◽  
Application Programming Interface ◽  
Practical Implementation ◽  
Blood Oxygen Level ◽  
Embedded Platform ◽  
Status Monitoring ◽  
The Family ◽  
Application Programming ◽  
Programming Interface

This article represents a prototype experiment regarding the practical implementation of an affordable, wireless, and online monitoring pulse oximetry device, for a Covid-19 infected patient that is quarantined at home. By using an Arduino based IoT (Internet of Things) embedded platform and a free online API (Application Programming Interface) live data stream server, the family physician can remotely monitor the patient’s pulse and blood oxygen level, by using a mobile phone, a tablet or a computer, without any contamination risk or contact, with the infected patient.

scholarly journals EpyNN: Educational python for Neural Networks

2021 ◽  
Author(s):  
Florian Malard ◽  
Laura Danner ◽  
Emilie Rouzies ◽  
Jesse G Meyer ◽  
Ewen Lescop ◽  
...  
Keyword(s):  
Neural Networks ◽  
Graphical Representation ◽  
Source Code ◽  
Application Programming Interface ◽  
Supplementary Information ◽  
Practical Implementation ◽  
Data Preparation ◽  
Link Type ◽  
Application Programming ◽  
Programming Interface

AbstractSummaryArtificial Neural Networks (ANNs) have achieved unequaled performance for numerous problems in many areas of Science, Business, Public Policy, and more. While experts are familiar with performance-oriented software and underlying theory, ANNs are difficult to comprehend for non-experts because it requires skills in programming, background in mathematics and knowledge of terminology and concepts. In this work, we release EpyNN, an educational python resource meant for a public willing to understand key concepts and practical implementation of scalable ANN architectures from concise, homogeneous and idiomatic source code. EpyNN contains an educational Application Programming Interface (API), educational workflows from data preparation to ANN training and a documentation website setting side-by-side code, mathematics, graphical representation and text to facilitate learning and provide teaching material. Overall, EpyNN provides basics for python-fluent individuals who wish to learn, teach or develop from scratch.AvailabilityEpyNN documentation is available at https://epynn.net and repository can be retrieved from https://github.com/synthaze/epynn.ContactStéphanie Olivier-Van-Stichelen, [email protected] InformationSupplementary files and listings.

National Heart, Lung, and Blood Institute cardiovascular clinical trial perspective

2020 ◽  
Vol 224 ◽  
pp. 25-34
Author(s):  
Gail D. Pearson ◽  
George A. Mensah ◽  
Yves Rosenberg ◽  
Catherine M. Stoney ◽  
Katherine Kavounis ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Blood Institute ◽  
National Heart ◽  
National Heart Lung

scholarly journals The impact of the National Heart, Lung, and Blood Institute data: analyzing published articles that used BioLINCC open access data

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 30
Author(s):  
Saif Aldeen AlRyalat ◽  
Osama El Khatib ◽  
Ola Al-qawasmi ◽  
Hadeel Alkasrawi ◽  
Raneem al Zu’bi ◽  
...  
Keyword(s):  
Open Data ◽  
Data Repository ◽  
Blood Institute ◽  
National Heart Lung ◽  
Pubmed Search ◽  
Number Of Publications ◽  
Almost All ◽  
Number Of Citations ◽  
The Impact ◽  
Access Data

Background: Data sharing is now a mandatory prerequisite for several major funders and journals, where researchers are obligated to deposit the data resulting from their studies in an openly accessible repository. Biomedical open data are now widely available in almost all disciplines, where researchers can freely access and reuse these data in new studies. We aim to assess the impact of open data in terms of publications generated using open data and citations received by these publications, where we will analyze publications that used the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) as an example. Methods: As of July 2019, there was a total of 194 datasets stored in BioLINCC repository and accessable through their portal. We requested the full list of publications that used these datasets from BioLINCC, and we also performed a supplementary PubMed search for other publications. We used Web of Science (WoS) to analyze the characteristics of publications and the citations they received. Results: 1,086 published articles used data from BioLINCC repository, but only 987 (90.88%) articles were WoS indexed. The number of publications has steadily increased since 2002 and peaked in 2018 with a total number of 138 publications on that year. The 987 open data publications received a total of 34,181 citations up to 1st October 2019. The average citation per item for the open data publications was 34.63. The total number of citations received by open data publications per year has increased from only 2 citations in 2002, peaking in 2018 with 2361 citations. Conclusion: The vast majority of studies that used BioLINCC open data were published in WoS indexed journals and are receiving an increasing number of citations.

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