Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparison of Environmental DNA and Bulk DNA Metabarcoding for Assessing Terrestrial Arthropod Diversity Across Three Habitat Types on Guam.

Molecular ecology resources·2026
Same author

Inpatient Characteristics and Outcomes of Venous Thromboembolism Among Children and Adolescents.

JAMA network open·2026
Same author

Quantifying Fidelity and Utility in Synthetic Healthcare Data.

Studies in health technology and informatics·2026
Same author

Setting up a DataSHIELD Hub for the German Medical Informatics Initiative: Challenges and Lessons Learned.

Studies in health technology and informatics·2026
Same author

Enabling Privacy-Preserving Federated Learning in Healthcare: The FLAME Architecture and Policy Framework.

Studies in health technology and informatics·2026
Same author

A Maturity Model for the Enforcement of PETs in Federated Settings.

Studies in health technology and informatics·2026
Same journal

A near telomere-to-telomere genome assembly of the tobacco root rot pathogen Fusarium oxysporum.

Scientific data·2026
Same journal

A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980-2024.

Scientific data·2026
Same journal

Phenotypic image dataset of naturally grown shiitake mushrooms across multiple varieties and growth stages.

Scientific data·2026
Same journal

A dataset supporting Combinatorial Proteome Integral Solubility/Stability Alteration Analysis (CoPISA).

Scientific data·2026
Same journal

Molecular Safeguards of Survival: De novo transcriptome assembly and tissue-specific transcriptomic profiling of the yellow-foot clam Paphia malabarica.

Scientific data·2026
Same journal

A unified spatial transcriptome profiling of ten mouse organs.

Scientific data·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

An In Vitro Model for the Study of Cellular Pathophysiology in Globoid Cell Leukodystrophy
07:45

An In Vitro Model for the Study of Cellular Pathophysiology in Globoid Cell Leukodystrophy

Published on: October 21, 2014

7.9K

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis.

Sascha Welten1, Marius de Arruda Botelho Herr2,3, Lars Hempel4,5,6

  • 1RWTH Aachen University, Chair of Computer Science 5, Aachen, 52074, Germany. welten@dbis.rwth-aachen.de.

Scientific Data
|June 22, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a technical framework for interoperable Personal Health Train (PHT) ecosystems, enabling distributed analytics across diverse infrastructures. The framework ensures data integration and security, facilitating cross-institutional research efficiently.

More Related Videos

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.3K
Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
07:13

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

4.2K

Related Experiment Videos

Last Updated: Jun 20, 2026

An In Vitro Model for the Study of Cellular Pathophysiology in Globoid Cell Leukodystrophy
07:45

An In Vitro Model for the Study of Cellular Pathophysiology in Globoid Cell Leukodystrophy

Published on: October 21, 2014

7.9K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.3K
Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
07:13

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

4.2K

Area of Science:

  • Health Informatics
  • Distributed Systems
  • Bioinformatics

Background:

  • Distributed analytics platforms are crucial for meeting data governance and legal constraints.
  • The Personal Health Train (PHT) is a key platform, but challenges arise when integrating multiple PHT infrastructures due to differing ecosystems.
  • Interoperability is essential for seamless data sharing and analysis across institutions with varied PHT setups.

Purpose of the Study:

  • To introduce a conceptual framework for achieving technical interoperability between different Personal Health Train (PHT) infrastructures.
  • To address challenges in data governance, regulatory compliance, and workflow modifications when combining multiple PHT ecosystems.
  • To enable distributed analytics across institutions by ensuring seamless data integration and analysis.

Main Methods:

  • Developed a conceptual framework focusing on five key requirements: data integration, unified station identifiers, mutual metadata, aligned security protocols, and business logic.
  • Evaluated the framework through a feasibility study involving two distinct PHT infrastructures: PHT-meDIC and PADME.
  • Analyzed patient data on leukodystrophy and differential diagnoses from University Hospitals of Tübingen, Leipzig, and Aachen.

Main Results:

  • Demonstrated technical interoperability between the PHT-meDIC and PADME infrastructures.
  • Enabled researchers to perform distributed analyses across participating institutions.
  • The proposed method is more space-efficient than multi-homing strategies with minimal time overhead.

Conclusions:

  • The conceptual framework successfully establishes technical interoperability for Personal Health Train (PHT) platforms.
  • This interoperability facilitates cross-institutional research by allowing unified data analysis.
  • The approach offers an efficient and effective solution for integrating diverse PHT ecosystems.