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: Patients with primary immunodeficiency secondary to abnormal recombinase activating genes (RAG) can present with broad clinical phenotypes ranging from early severe infections to autoimmune complications and inflammation. Immunological phenotype may also vary from TB severe combined immunodeficiency to combined immunodeficiency or antibody deficiencies with near-normal T and B cell counts and even preserved specific antibody response to pathogens. It is not uncommon that RAG variants of uncertain significance are identified by serendipity during a broad genetic screening process and pathogenic RAG variants are increasingly recognized among all age groups, including adults. Establishing the pathogenicity and clinical relevance of novel RAG variants can be challenging since RAG genes are highly polymorphic. This review paper aims to summarize clinical phenotypes of RAG deficiencies and provide practical guidance for confirming the direct link between specific RAG variants and clinical disease. Lastly, we will review the current understanding of treatment option for patients with varying severity of RAG deficiencies. : This review discusses the different phenotypes and immunological aspects of RAG deficiencies, the diagnosis dilemma facing clinicians, and an overview of current and advancement in treatments. : A careful analysis of immunological and clinical data and their correlation with genetic findings helps to determine the significance of the genetic polymorphism. Advances in functional assays, as well as anti-cytokine antibodies, make it easier to resolve the diagnostic dilemma.
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http://dx.doi.org/10.1080/1744666X.2020.1670060 | DOI Listing |
Comput Biol Med
August 2025
School of Medical, Indigenous and Health Sciences, University of Wollongong, Wollongong, Australia.
Despite rapid healthcare digitization, extracting information from unstructured electronic health records (EHRs), such as nursing notes, remains challenging due to inconsistencies and ambiguities in clinical documentation. Generative large language models (LLMs) have emerged as promising tools for automating information extraction (IE); however, their application in real-world clinical settings, such as residential aged care (RAC), is limited by critical gaps. Prior studies have often focused on structured EHR data and conventional evaluation metrics such as accuracy and F1 score, overlooking critical aspects like robustness, fairness, bias, and contextual relevance, particularly in unstructured clinical narratives.
View Article and Find Full Text PDFBioengineering (Basel)
August 2025
Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA.
Retrieval-Augmented Generation (RAG) offers a promising strategy to harness large language models (LLMs) for delivering up-to-date, accurate clinical guidance while reducing physicians' cognitive burden, yet its effectiveness hinges on query clarity and structure. We propose an adaptive Self-Query Retrieval (SQR) framework that integrates three refinement modules-PICOT (Population, Intervention, Comparison, Outcome, Time), SPICE (Setting, Population, Intervention, Comparison, Evaluation), and Iterative Query Refinement (IQR)-to automatically restructure and iteratively enhance clinical questions until they meet predefined retrieval-quality thresholds. Implemented on Gemini-1.
View Article and Find Full Text PDFSci Transl Med
July 2025
Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
Hypertrophic cardiomyopathy (HCM) is a common, serious, genetic heart muscle disorder. Although the biophysical mechanisms by which gene variants in sarcomeric proteins disrupt cardiomyocyte function are largely understood, the cellular and molecular pathways leading to the complex, variable, and adverse remodeling of the non-myocyte compartment are unexplained. Here, we report that postmortem and explanted human HCM hearts exhibited chronic focal leukocyte infiltration and prominent activation of immune cells.
View Article and Find Full Text PDFInt J Med Inform
November 2025
University Medical Center Hamburg-Eppendorf / University Cancer Center, Martinistr. 52, Hamburg, 22767, Germany. Electronic address:
Structured oncological documentation is vital for data-driven cancer care, yet extracting clinical features from unstructured pathology reports remains challenging-especially in German healthcare, where strict data protection rules require local model deployment. This study evaluates open-source large language models (LLMs) for extracting oncological attributes from German pathology reports in a secure, on-premise setting. We created a gold-standard dataset of 522 annotated reports and developed a retrieval-augmented generation (RAG) pipeline using an additional 15,000 pathology reports.
View Article and Find Full Text PDFBioinformatics
July 2025
Department of Computer Science, School of Computing and Data Science, University of Hong Kong, Hong Kong, 999077, China.
Motivation: Rare diseases affect over 300 million people worldwide and are often caused by genetic variants. While variant detection has become cost-effective, interpreting these variants-particularly collecting literature-based evidence like ACMG/AMP PM3-remains complex and time-consuming.
Results: We present AutoPM3, a method that automates PM3 evidence extraction from literatures using open-source large language models (LLMs).