Background: Many patients with rare diseases are still lacking a timely diagnosis and approved therapies for their condition despite the tremendous efforts of the research community, biopharmaceutical, medical device industries, and patient support groups. The development of clinical research networks for rare diseases offers a tremendous opportunity for patients and multi-disciplinary teams to collaborate, share expertise, gain better understanding on specific rare diseases, and accelerate clinical research and innovation. Clinical Research Networks have been developed at a national or continental level, but global collaborative efforts to connect them are still lacking. The International Rare Diseases Research Consortium set a Task Force on Clinical Research Networks for Rare Diseases with the objective to analyse the structure and attributes of these networks and to identify the barriers and needs preventing their international collaboration. The Task Force created a survey and sent it to pre-identified clinical research networks located worldwide.

Results: A total of 34 responses were received. The survey analysis demonstrated that clinical research networks are diverse in their membership composition and emphasize community partnerships including patient groups, health care providers and researchers. The sustainability of the networks is mostly supported by public funding. Activities and research carried out at the networks span the research continuum from basic to clinical to translational research studies. Key elements and infrastructures conducive to collaboration are well adopted by the networks, but barriers to international interoperability are clearly identified. These hurdles can be grouped into five categories: funding limitation; lack of harmonization in regulatory and contracting process; need for common tools and data standards; need for a governance framework and coordination structures; and lack of awareness and robust interactions between networks.

Conclusions: Through this analysis, the Task Force identified key elements that should support both developing and established clinical research networks for rare diseases in implementing the appropriate structures to achieve international interoperability worldwide. A global roadmap of actions and a specific research agenda, as suggested by this group, provides a platform to identify common goals between these networks.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169162PMC
http://dx.doi.org/10.1186/s13023-023-02650-4DOI Listing

Publication Analysis

Top Keywords

clinical networks
28
rare diseases
28
networks rare
16
task force
16
international interoperability
12
networks
12
clinical
9
key elements
8
rare
7
diseases
7

Similar Publications

Dysregulation of long non-coding RNAs (lncRNAs) is implicated in the pathophysiology of ischemic stroke (IS). However, the molecular mechanism of the lncRNA SERPINB9P1 in IS remains unclear. Our study aimed to explore the role and molecular mechanism of the lncRNA SERPINB9P1 in IS.

View Article and Find Full Text PDF

Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach.

Med Biol Eng Comput

January 2025

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.

View Article and Find Full Text PDF

Limited treatment options are available for bladder cancer (BCa) resulting in extremely high mortality rates. Cyclovirobuxine D (CVB-D), a naturally alkaloid, reportedly exhibits notable antitumor activity against diverse tumor types. However, its impact on CVB-D on BCa and its precise molecular targets remain unexplored.

View Article and Find Full Text PDF

In 2002, in the Emilia-Romagna region of Italy, a comprehensive strategic plan was developed with the aim of improving the integration and efficiency of the genetic services. Two decades later, this report aims to explore the current functioning of the regional network, with special focus on clinical genetics in the evolving scenarios. To this aim, we analyzed the activity data of the medical genetics services in the region, to identify and possibly improve currently open issues.

View Article and Find Full Text PDF

Deep Neural Network Analysis of the 12-Lead Electrocardiogram Distinguishes Patients With Congenital Long QT Syndrome From Patients With Acquired QT Prolongation.

Mayo Clin Proc

January 2025

Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN; Department of Molecular Pharmacology and Experimental Therapeutics, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN; Division of Heart Rhythm Services, Department of Cardiovascular Medicine, Windland Smith Rice Genetic Heart Rhythm Clinic, Mayo Clinic, Rochester, MN. Electronic address:

Objective: To test whether an artificial intelligence (AI) deep neural network (DNN)-derived analysis of the 12-lead electrocardiogram (ECG) can distinguish patients with long QT syndrome (LQTS) from those with acquired QT prolongation.

Methods: The study cohort included all patients with genetically confirmed LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic's ECG data vault comprising more than 2.5 million patients.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!