ReviewSeminars in arthritis and rheumatism2020
The basic and translational science year in review: Confucius in the era of Big Data.
Review in Seminars in arthritis and rheumatism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Meta-analysis and transcriptomic analysis reveal that NKRF and ZBTB17 regulate the NF-κB signaling pathway, contributing to the shared molecular mechanisms of Alzheimer's disease and atherosclerosis.CNS neuroscience & therapeutics · 2024Pooled it
- Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling.Current rheumatology reports · 2024Review
- Unraveling the Immunopathogenesis and Genetic Variants in Vasculitis Toward Development of Personalized Medicine.Frontiers in cardiovascular medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Personalized medicine is an important goal for the treatment of rheumatic disease that seeks to improve outcomes by matching therapy more precisely with the underlying pathogenetic disturbances in the individual patient. Realization of this goal requires actionable biomarkers to identify these disturbances as well as pathways that can be targeted for novel therapy. Among advances in characterizing pathogenesis, Big Data provides an unprecedented picture of pathogenesis, with analysis of tissue lesions revealing disturbances that may not be apparent in blood. Big Data approaches include single cell RNAseq (scRNAseq) which can elucidate patterns of gene expression by individual cells. Galvanized by the Accelerating Medicines Partnership, a public-private initiative of the NIH, investigative teams have analyzed gene expression in cells in the synovium for rheumatoid arthritis and kidney for systemic lupus erythematosus. A review of basic and translational research for 2018-2019 provides the progress in these areas. Thus, the studies on rheumatoid arthritis have identified subpopulations of immune cells and fibroblasts implicated in synovitis. For lupus, transcriptomic studies have provided evidence for widespread effects of type 1 interferon. Studies in progressive sclerosis have demonstrated changes associated with stem cell therapy as well as potential new targets for anti-fibrotic agents. Other studies using molecular approaches have defined new mechanisms for vasculitis as well as the potential role of the microbiome in inflammatory arthritis and systemic lupus erythematosus. Future studies with Big Data will incorporate the spatial relationships of cells in inflammation as well as changes in gene expression over time.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.