Background Bardet–Biedl syndrome (BBS) is a recessive and genetically heterogeneous ciliopathy characterised by retinitis pigmentosa, obesity, kidney dysfunction, postaxial polydactyly, behavioural dysfunction and hypogonadism. 7 of the 17 BBS gene products identified to date assemble together with the protein BBIP1/BBIP10 into the BBSome, a protein complex that ferries signalling receptors to and from cilia. Methods and results Exome sequencing performed on a sporadic BBS case revealed for the first time a homozygous stop mutation (NM_001195306: c.173T>G, p.Leu58*) in the BBIP1 gene. This mutation is pathogenic since no BBIP1 protein could be detected in fibroblasts from the patient, and BBIP1[Leu58*] is unable to associate with the BBSome subunit BBS4. Conclusions These findings identify BBIP1 as the 18th BBS gene (BBS18) and suggest that BBSome assembly may represent a unifying pathomechanism for BBS.

The diffusion of next-generation sequencing technologies has revolutionized research and diagnosis in the field of rare Mendelian disorders, notably via whole-exome sequencing (WES). However, one of the main issues hampering achievement of a diagnosis via WES analyses is the extended list of variants of unknown significance (VUS), mostly composed of missense variants. Hence, improved solutions are needed to address the challenges of identifying potentially deleterious variants and ranking them in a prioritized short list. We present MISTIC (MISsense deleTeriousness predICtor), a new prediction tool based on an original combination of two complementary machine learning algorithms using a soft voting system that integrates 113 missense features, ranging from multi-ethnic minor allele frequencies and evolutionary conservation, to physiochemical and biochemical properties of amino acids. Our approach also uses training sets with a wide spectrum of variant profiles, including both high-confidence positive (deleterious) and negative (benign) variants. Compared to recent state-of-the-art prediction tools in various benchmark tests and independent evaluation scenarios, MISTIC exhibits the best and most consistent performance, notably with the highest AUC value (> 0.95). Importantly, MISTIC maintains its high performance in the specific case of discriminating deleterious variants from benign variants that are rare or population-specific. In a clinical context, MISTIC drastically reduces the list of VUS (<30%) and significantly improves the ranking of “causative” deleterious variants. Pre-computed MISTIC scores for all possible human missense variants are available at http://lbgi.fr/mistic.

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: In France, clinical data on rare diseases are primarily collected through BaMaRa (Base Maladies Rares), a software platform used by national expert centers to populate the BNDMR (Banque Nationale de Données Maladies Rares), the French national rare disease data warehouse. BaMaRa ensures standardized and structured data collection across all rare disease networks, with a focus on care coordination and epidemiological reporting. In 2024, FREDD (French Rare Eye Disease Database), a health data warehouse dedicated to rare eye diseases, was developed within the framework of the third French National Rare Disease Plan by the SENSGENE sector. Despite overlapping datasets, there is no native interoperability between BaMaRa and FREDD, requiring the development of a dedicated, traceable pipeline to transform BaMaRa exports into data suitable for inclusion in FREDD. This transformation involves complex business rules to address structural, semantic, and specific differences between the two systems. Objective: This study aims to describe the design and implementation of a robust data transformation pipeline that enables the automated conversion of BaMaRa clinical records into a structured dataset aligned with the FREDD data model. The primary goal is to ensure that the data remain semantically consistent and reusable for the secondary use of health data. Methods: We developed a Python-based application called FREDDEX that integrates several configuration files and encodes the domain-specific business rules required to align BaMaRa data with the FREDD schema. These rules include patient filtering, mapping of variable names and values, management of multisource redundancy, and prevention of overwriting. The system was designed to be modular, auditable, and usable by clinical data managers with minimal technical expertise. Results: FREDDEX was tested and validated on a BaMaRa export of 1000 real patients from Strasbourg University Hospital. The tool successfully filtered and created 641 patient profiles in FREDD, with a 99% success rate for attempted imports and full concordance (100%) for directly mapped and inferred variables. Genetic data reconstruction was confirmed on a random sample of 30 patients with genetic information, showing 100% accuracy, and multidiagnostic blocks were correctly handled in all manually reviewed cases. Beyond validation, FREDDEX processed up to 5000 patient records, enabling the rapid onboarding of new clinical sites and significantly reducing manual curation time, while runtime and memory usage demonstrated near-linear scaling. Importantly, the tool established a facilitated reproducible framework adaptable to other rare disease contexts and interoperable with national and European platforms, such as European Reference Network-EYE. Conclusions: This work demonstrates that transforming structured national rare disease registry data into a research-oriented health data warehouse is feasible when clinical business rules are explicitly formalized within an auditable extract-transform-load framework. Beyond the FREDD use case, this approach illustrates how interoperability between care-based and research infrastructures can be operationalized in rare diseases while preserving semantic integrity and regulatory compliance.

Medical acts, such as imaging, lead to the production of several medical text report that describes the relevant findings. This induces multimodality in patient data by linking image data to free-text and consequently, multimodal data have become central to drive research and improve diagnosis. However, the exploitation of patient data is challenging as the ecosystem of analysis tools is fragmented depending on the type of data (images, text, genetics), the task (processing, exploration) and domains of interest (clinical phenotype, histology). To address the challenges, we present IMPatienT ( I ntegrated digital M ultimodal PATIEN t da T a), a simple, flexible and open-source web application to digitize, process and explore multimodal patient data. IMPatienT has a modular architecture to: (i) create a standard vocabulary for a domain, (ii) digitize and process free-text data, (iii) annotate images and perform image segmentation, and (iv) generate a visualization dashboard and perform diagnosis suggestions. We showcased IMPatienT on a corpus of 40 simulated muscle biopsy reports of congenital myopathy patients. As IMPatienT relies on a user-designed vocabulary, it can be adapted to any domain of research and can be used as a patient registry for exploratory data analysis (EDA). A demo instance of the application is available at https://impatient.lbgi.fr/ .