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  • American Association for the Advancement of Science (AAAS)  (2)
  • 1
    Online Resource
    Online Resource
    Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis
    American Association for the Advancement of Science (AAAS) ; 2020
    In:  Science Translational Medicine Vol. 12, No. 545 ( 2020-05-27)
    Details
    In: Science Translational Medicine, American Association for the Advancement of Science (AAAS), Vol. 12, No. 545 ( 2020-05-27)
    Abstract: It is challenging to quickly diagnose slowly progressing diseases. To prioritize multiple related diagnoses, we developed G-PROB (Genetic Probability tool) to calculate the probability of different diseases for a patient using genetic risk scores. We tested G-PROB for inflammatory arthritis–causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout). After validating on simulated data, we tested G-PROB in three cohorts: 1211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database, 245 patients identified through ICD codes and medical record review within the Partners Biobank, and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. Calibration of G-probabilities with disease status was high, with regression coefficients from 0.90 to 1.08 (1.00 is ideal). G-probabilities discriminated true diagnoses across the three cohorts with pooled areas under the curve (95% CI) of 0.69 (0.67 to 0.71), 0.81 (0.76 to 0.84), and 0.84 (0.81 to 0.86), respectively. For all patients, at least one disease could be ruled out, and in 45% of patients, a likely diagnosis was identified with a 64% positive predictive value. In 35% of cases, the clinician’s initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease, which improved to 51% ( P 〈 0.0001) after adding G-probabilities. Converting genotype information before a clinical visit into an interpretable probability value for five different inflammatory arthritides could potentially be used to improve the diagnostic efficiency of rheumatic diseases in clinical practice.
    Type of Medium: Online Resource
    ISSN: 1946-6234 , 1946-6242
    URL: Article
    DOI: 10.1126/scitranslmed.aay1548
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2020
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Mixed-effects association of single cells identifies an expanded effector CD4 + T cell subset in rheumatoid arthritis
    American Association for the Advancement of Science (AAAS) ; 2018
    In:  Science Translational Medicine Vol. 10, No. 463 ( 2018-10-17)
    Details
    In: Science Translational Medicine, American Association for the Advancement of Science (AAAS), Vol. 10, No. 463 ( 2018-10-17)
    Abstract: High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4 + T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4 + T cells, identified as CD27 − HLA-DR + effector memory cells, in RA patients (odds ratio, 1.7; P = 1.1 × 10 −3 ). The frequency of CD27 − HLA-DR + cells was similarly elevated in blood samples from a second RA patient cohort, and CD27 − HLA-DR + cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27 − HLA-DR + cells were associated with RA (meta-analysis P = 2.3 × 10 −4 ). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27 − HLA-DR + cells, which comprised ~10% of synovial CD4 + T cells. CD27 − HLA-DR + cells expressed a distinctive effector memory transcriptomic program with T helper 1 (T H 1)– and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data.
    Type of Medium: Online Resource
    ISSN: 1946-6234 , 1946-6242
    URL: Article
    DOI: 10.1126/scitranslmed.aaq0305
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2018
    Location Call Number Limitation Availability
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    Online Resource
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