Each month, we share summaries of recent Rare Diseases Clinical Research Network (RDCRN) grant-funded publications. Catch up on the latest RDCRN research below.
Jump to:
- Congenital and Perinatal Infections Consortium (CPIC)
- Consortium of Eosinophilic Gastrointestinal Disease Researchers (CEGIR)
- Developmental Synaptopathies Consortium (DSC)
- Myasthenia Gravis Rare Disease Network (MGNet)
Listen to these summaries on the Rare Research Report podcast.
Congenital and Perinatal Infections Consortium (CPIC)
Exploring Prevention of Congenital Cytomegalovirus Infection
Congenital cytomegalovirus (CMV) is a herpes viral infection that occurs before birth. CMV is a common virus that affects people of all ages and usually does not cause symptoms in healthy children and adults. However, some babies born with CMV can have health problems at birth or that develop later, including neurodevelopmental delay and hearing loss.
Over the past 30 years, many new strategies for prevention of congenital CMV infection have emerged. These include education initiatives, behavioral modifications, and maternal antiviral prophylaxis.
In this review, authors explore different levels of congenital CMV prevention, including the potential for development of effective vaccines for CMV.
Vipulanandan Y, Boppana S, Fowler KB, Kimberlin DW. Advancements and potential in the prevention of congenital CMV infection. Semin Fetal Neonatal Med. 2025 Sep 25:101662. doi: 10.1016/j.siny.2025.101662. Epub ahead of print. PMID: 41044015; PMCID: PMC12614814.
Consortium of Eosinophilic Gastrointestinal Disease Researchers (CEGIR)
Exploring the Use of Artificial Intelligence Tools in the Detection and Management of Eosinophilic Gastrointestinal Disorders
Eosinophilic gastrointestinal disorders (EGIDs) are a group of chronic immune system disorders in which a type of white blood cell (eosinophils) build up in the gastrointestinal tract, causing inflammation or injury. Some types of EGIDs—particularly non-eosinophilic esophagitis (EoE)—are not well characterized, with management relying on expert opinion.
In this article, researchers from the Consortium of Eosinophilic Gastrointestinal Disease Researchers (CEGIR)—including the principial investigator and two scholars—collaborate to explore the use of artificial intelligence (AI) tools in the detection and management of EGIDs. The team outlines present and future AI applications, including prediction of disease trajectories, personalization of treatment, clinical decision support, patient education, and clinical monitoring.
Authors note that although AI holds potential to enhance EGID diagnosis and management, realizing this promise will require nuanced, multifaceted evaluation of its ability to positively transform research and clinical practice.
Ketchem CJ, Gabryszewski SJ, Rothenberg ME. Artificial intelligence in the detection and management of eosinophilic gastrointestinal diseases: Applications, challenges, and future outlook. J Allergy Clin Immunol. 2025 Sep 4:S0091-6749(25)00936-4. doi: 10.1016/j.jaci.2025.08.020. Epub ahead of print. PMID: 40914297; PMCID: PMC12543360.
Developmental Synaptopathies Consortium (DSC)
Characterizing Key Factors that Correlate with Sleep Problems in Rare Neurodevelopmental Genetic Disorders
Neurodevelopmental genetic disorders (NGDs) are a spectrum of conditions that affect how the brain functions. Individuals with NGDs often experience sleep problems, which also affects their ability to function during the daytime. Despite these common issues, not much is known about predictors of sleep problems in NGDs.
In this study, researchers characterized key factors that correlate with sleep problems in rare NGDs. Parents of 173 individuals with rare NGDs—including PTEN hamartoma tumor syndrome, SYNGAP1, NFIX, and a mixed group of other NGDs—completed the Neurobehavioral Evaluation Tool. The team used these evaluations to characterize sleep phenotypes across disorders and examine predictors of poor sleep.
Results highlighted the elevated severity of sleep problems in NGDs, particularly in those with SYNGAP1. Predictors for each sleep problem varied, suggesting that accurate assessment and diagnosis of sleep problems—as well as evaluation of correlates of sleep difficulties—are required in order to provide targeted interventions in rare NGDs.
Baker EK, Frazier TW, Phillips JM, Hardan AY, Uljarević M. Characterizing Key Correlates of Sleep Problems Across Rare Neurodevelopmental Genetic Disorders. J Autism Dev Disord. 2025 Dec;55(12):4480-4491. doi: 10.1007/s10803-025-07069-3. Epub 2025 Oct 25. PMID: 41138043.
Developing a Fully Automated Algorithm for Tuber Segmentation and Quantification of Tuber Volume in Tuberous Sclerosis Complex
Tuberous sclerosis complex (TSC) is a genetic disorder that leads to the growth of non-cancerous tumors in multiple organs. In the brain, these include “tubers,” areas of abnormal tissue just beneath the cortical surface that can cause seizures and disrupt normal brain function. Magnetic resonance imaging (MRI) is commonly used to identify how many tubers are present, how large they are, and where they are located. These features often relate to the type and severity of a person’s neurological symptoms.
In this study, researchers created a fully automated neural network (an artificial intelligence-based program) to detect tubers on MRI and measure their total volume. They trained the model using 263 brain MRI scans from 196 individuals with TSC. They then compared the algorithm’s performance with measurements made by an expert neuroradiologist.
The algorithm’s estimates of total tuber load showed an almost perfect match with the expert standard. The authors conclude that this tool provides an objective and consistent way to identify and measure tubers, which may improve the reliability of TSC research across different sites.
Sánchez Fernández I, Soldatelli MD, Miller GN, Gout CF, Broekhuizen EC, den Hertog ICJ, Pijs DA, Apostolopoulos E, Kaur P, Ouaalam A, Bebin ME, Northrup H, Krueger DA, Wu JY, Cohen AL, Sahin M, Karimi D, Warfield SK, Peters JM; TACERN Study Group and the Developmental Synaptopathies Consortium–RDCRN Study Group. Convolutional neural networks for automatic tuber segmentation and quantification of tuber burden in tuberous sclerosis complex. Epilepsia. 2025 Nov 19. doi: 10.1111/epi.70007. Epub ahead of print. PMID: 41258699.
Myasthenia Gravis Rare Disease Network (MGNet)
Building a Quantitative Telemedicine Platform for Myasthenia Gravis
Myasthenia gravis (MG) is a neuromuscular disorder caused by an autoimmune response which blocks or damages acetylcholine receptors in muscles, causing disabling weakness. Detailed physical examinations are required to help understand the fluctuation of symptoms, presenting unique challenges for remote assessment.
In this study, researchers review how they built a quantitative telemedicine platform for evaluation of patients with MG. The platform augments traditional neurological assessments using computer vision, signal processing, and augmented intelligence. To develop the technology, the team used video examinations of 52 patients with MG recorded twice, applying machine learning algorithms to extract clinically relevant features from video and audio data.
Results revealed that variations in examiner instructions and video quality significantly affect reliability. Authors note that a digital examination framework can enhance MG assessment precision, reduce variability in physical examination evaluation, and support the telemedicine examination.
Garbey M, Lesport Q, Öztosun G, Kaminski HJ. Building a Quantitative Telemedicine Platform for Myasthenia Gravis: Augmenting the Physical Examination. Muscle Nerve. 2025 Nov 20. doi: 10.1002/mus.70072. Epub ahead of print. PMID: 41263248.
The Rare Diseases Clinical Research Network (RDCRN) is funded by the National Institutes of Health (NIH) and led by the National Center for Advancing Translational Sciences (NCATS) through its Division of Rare Diseases Research Innovation (DRDRI). Now in its fifth five-year funding cycle, RDCRN is a partnership with funding and programmatic support provided by Institutes, Centers, and Offices across NIH, including the National Institute of Neurological Disorders and Stroke, the National Institute of Allergy and Infectious Diseases, the National Institute of Diabetes and Digestive and Kidney Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Heart, Lung, and Blood Institute, the National Institute of Dental and Craniofacial Research, the National Institute of Mental Health, the Office of Dietary Supplements, the National Institute on Aging, the National Human Genome Research Institute, the National Institute on Deafness and Other Communication Disorders, and the Office of Research on Women’s Health.
