Rare Research Report

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Rare Research Report features summaries of recent scientific publications from the Rare Diseases Clinical Research Network, which is funded by the National Institutes of Health. The network includes 20 active consortia—teams of researchers, patients, and clinicians—each focused on a group of rare disorders. Join us for new episodes each month. Learn more about the RDCRN: https://www.rarediseasesnetwork.org

  1. ٢٨ يناير

    BBDC: Assessing Temporomandibular Joints in Patients with Osteogenesis Imperfecta

    New research from the Brittle Bone Disorders Consortium (BBDC). This summary is based on a paper published in the journal Oral Surgery, Oral Medicine, Oral Pathology, and Oral Radiology on September 16, 2025, titled "Quantitative assessment of the temporomandibular joints in patients with osteogenesis imperfecta: a CBCT study."  Read the paper here.  Learn more about BBDC.  Transcript:  New research from the Brittle Bone Disorders Consortium (BBDC), a research group of the Rare Diseases Clinical Research Network. Assessing Temporomandibular Joints in Patients with Osteogenesis Imperfecta.  This summary is based on a paper published in the journal Oral Surgery, Oral Medicine, Oral Pathology, and Oral Radiology on September 16, 2025. Osteogenesis imperfecta (OI) is a group of inherited connective tissue disorders associated with a wide range of symptoms, including fragile bones that break easily. Individuals with OI can experience problems with bone formation and function. In this study, researchers assessed temporomandibular joints—which connect the jawbone to the skull—in patients with OI. First, the team used cone-beam computed tomography (CBCT) to create 3D images of the temporomandibular joints of 48 OI patients and 48 age- and sex-matched controls. Next, they evaluated mandibular condylar volume and height. Results showed that individuals with OI had significantly reduced condylar volume and height. Authors note that these findings indicate impaired and delayed condylar development consistent with overall skeletal maturation delay in OI.

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  2. ٢٨ يناير

    SP-CERN: Evaluating Plasma Neurofilament Light Chain as a Biomarker for Hereditary Spastic Paraplegia-SPG11 and -ZFYVE26

    New research from the Spastic Paraplegia Centers of Excellence Research Network (SP-CERN). This summary is based on a paper published in the journal Movement Disorders on December 9, 2025, titled "Longitudinal Dynamics of Plasma Neurofilament Light Chain in Hereditary Spastic Paraplegia Type 11 (HSP-SPG11) and Type 15 (HSP-ZFYVE26)."  Read the paper here.  Learn more about SP-CERN.  Transcript:  New research from the Spastic Paraplegia Centers of Excellence Research Network (SP-CERN), a research group of the Rare Diseases Clinical Research Network. Evaluating Plasma Neurofilament Light Chain as a Biomarker for Hereditary Spastic Paraplegia-SPG11 and -ZFYVE26.  This summary is based on a paper published in the journal Movement Disorders on December 9, 2025.  Hereditary spastic paraplegia (HSP) is a large group of inherited disorders that affect nerves that send messages to the muscles. HSP-SPG11 and HSP-ZFYVE26 are autosomal-recessive forms of HSP, meaning that they are caused by two mutated copies of a gene. More information is needed about measurable signs of these disorders for new therapeutic trials. In this study, researchers evaluated plasma neurofilament light chain (pNfL) as a biomarker for HSP-SPG11 and HSP-ZFYVE26. The team analyzed pNfL levels in 57 patients with HSP, collecting clinical and biomarker data over five years. Results showed significantly elevated baseline pNfL levels in patients with HSP, reflecting early neuroaxonal injury. However, authors note that baseline pNfL did not help predict future disease progression.

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  3. ٢٨ يناير

    SP-CERN: Exploring the Spectrum of Movement Disorders in Early-Onset Hereditary Spastic Paraplegia

    New research from the Spastic Paraplegia Centers of Excellence Research Network (SP-CERN). This summary is based on a paper published in the journal Movement Disorders on December 2, 2025, titled "Spectrum of Movement Disorders in Early-Onset Hereditary Spastic Paraplegia: A Study of 428 Cases."  Read the paper here.  Learn more about SP-CERN.  Transcript:  New research from the Spastic Paraplegia Centers of Excellence Research Network (SP-CERN), a research group of the Rare Diseases Clinical Research Network. Exploring the Spectrum of Movement Disorders in Early-Onset Hereditary Spastic Paraplegia. This summary is based on a paper published in the journal Movement Disorders on December 2, 2025. Hereditary spastic paraplegia (HSP) is a large group of inherited disorders that affect nerves that send messages to the muscles. Individuals with early-onset HSP can experience movement disorders, but not much is known about why and how often they occur. In this study, researchers explored the spectrum of movement disorders in early-onset HSP. The team analyzed data from 428 children and young adults with HSP, reviewing clinical characteristics and video examinations. Results showed that movement disorders—including dystonia, parkinsonism, and ataxia—were common in childhood-onset HSP. Authors note that routine screening and management tailored to specific genotypes of movement disorders—especially dystonia—may improve functional outcomes and quality of life.

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  4. ٢٢‏/١٢‏/٢٠٢٥

    CPIC: Exploring Prevention of Congenital Cytomegalovirus Infection

    New research from the Congenital and Perinatal Infections Consortium (CPIC). This summary is based on a paper published in the journal Seminars in Fetal and Neonatal Medicine on September 25, 2025, titled "Advancements and potential in the prevention of congenital CMV infection."  Read the paper here.  Learn more about CPIC.  Transcript:  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.

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  5. ٢٢‏/١٢‏/٢٠٢٥

    CEGIR: Exploring the Use of Artificial Intelligence Tools in the Detection and Management of Eosinophilic Gastrointestinal Disorders

    New research from the Consortium of Eosinophilic Gastrointestinal Disease Researchers (CEGIR). This summary is based on a paper published in the Journal of Allergy and Clinical Immunology on September 4, 2025, titled "Artificial intelligence in the detection and management of eosinophilic gastrointestinal diseases: Applications, challenges, and future outlook."  Read the paper here.  Learn more about CEGIR: https://cegir.rarediseasesnetwork.org Transcript:  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 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.

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  6. ٢٢‏/١٢‏/٢٠٢٥

    DSC: Characterizing Key Factors that Correlate with Sleep Problems in Rare Neurodevelopmental Genetic Disorders

    New research from the Developmental Synaptopathies Consortium (DSC). This summary is based on a paper published in the Journal of Autism and Developmental Disorders on October 25, 2025, titled "Characterizing Key Correlates of Sleep Problems Across Rare Neurodevelopmental Genetic Disorders."  Read the paper here.  Learn more about DSC.  Transcript:  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.

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    DSC: Developing a Fully Automated Algorithm for Tuber Segmentation and Quantification of Tuber Volume in Tuberous Sclerosis Complex

    New research from the Developmental Synaptopathies Consortium (DSC). This summary is based on a paper published in the journal Epilepsia on November 19, 2025, titled "Convolutional neural networks for automatic tuber segmentation and quantification of tuber burden in tuberous sclerosis complex."  Read the paper here.  Learn more about DSC.  Transcript:  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.

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    MGNet: Building a Quantitative Telemedicine Platform for Myasthenia Gravis

    New research from the Myasthenia Gravis Rare Disease Network (MGNet). This summary is based on a paper published in the journal Muscle & Nerve on November 20, 2025, titled "Building a Quantitative Telemedicine Platform for Myasthenia Gravis: Augmenting the Physical Examination."  Read the paper here.  Learn more about MGNet.  Transcript:  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.

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Rare Research Report features summaries of recent scientific publications from the Rare Diseases Clinical Research Network, which is funded by the National Institutes of Health. The network includes 20 active consortia—teams of researchers, patients, and clinicians—each focused on a group of rare disorders. Join us for new episodes each month. Learn more about the RDCRN: https://www.rarediseasesnetwork.org

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