Primary Immunodeficiencies (PIDs) are associated with more than 400 rare monogenic diseases affecting various biological functions (e.g., development, regulation of the immune response) with a heterogeneous clinical expression (from no symptom to severe manifestations). To better understand PIDs, the ATRACTion project aims to perform a multi-omics analysis of PIDs cases versus a control group patients, including single-cell transcriptomics, epigenetics, proteomics, metabolomics, metagenomics and lipidomics. In this study, our goal is to develop a common data model integrating clinical and omics data, which can be used to obtain standardized information necessary for characterization of PIDs patients and for further systematic analysis. For that purpose, we extend the OMOP Common Data Model (CDM) and propose a multi-omics ATRACTion OMOP-CDM to integrate multi-omics data. This model, available for the community, is customizable for other types of rare diseases (https://framagit.org/imagine-plateforme-bdd/pub-rhu4-atraction).
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http://dx.doi.org/10.3233/SHTI220031 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Med Phys
January 2025
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Background: Diffusing alpha-emitters Radiation Therapy ("Alpha DaRT") is a promising new radiation therapy modality for treating bulky tumors. Ra-carrying sources are inserted intratumorally, producing a therapeutic alpha-dose region with a total size of a few millimeter via the diffusive motion of Ra's alpha-emitting daughters. Clinical studies of Alpha DaRT have reported 100% positive response (30%-100% shrinkage within several weeks), with post-insertion swelling in close to half of the cases.
View Article and Find Full Text PDFClin Pharmacokinet
January 2025
Facultés de Médecine et de Pharmacie de Lyon, Univ Lyon, Université Claude Bernard Lyon 1, Lyon, France.
Background And Objective: Limited information is available on the pharmacokinetics of rifampicin (RIF) along with that of its active metabolite, 25-deacetylrifampicin (25-dRIF). This study aimed to analyse the pharmacokinetic data of RIF and 25-dRIF collected in adult patients treated for tuberculosis.
Methods: In adult patients receiving 10 mg/kg of RIF as part of a standard regimen for drug-susceptible pulmonary tuberculosis enrolled in the Opti-4TB study, plasma RIF and 25-dRIF concentrations were measured at various occasions.
Brain Imaging Behav
January 2025
Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiation Oncology, Henry Ford Health, Detroit, MI, USA.
Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models promise automation, they lack validation on interventional angiographic data. This study investigates the feasibility of angiographic segmentation using in-context learning with the UniverSeg model, which is a cross-learning segmentation model that lacks inherent angiographic training.
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