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Ines Colmegna

Researcher at McGill University

Publications -  96
Citations -  2374

Ines Colmegna is an academic researcher from McGill University. The author has contributed to research in topics: Medicine & Mesenchymal stem cell. The author has an hindex of 21, co-authored 77 publications receiving 1882 citations. Previous affiliations of Ines Colmegna include LSU Health Sciences Center New Orleans & Emory University.

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Pathogen-Sensing Plasmacytoid Dendritic Cells Stimulate Cytotoxic T-Cell Function in the Atherosclerotic Plaque Through Interferon-α

TL;DR: In this paper, plasmacytoid dendritic cells (pDCs) that specialize in sensing bacterial and viral products can regulate effector functions of plaque-residing T cells and thus connect host infection and plaque instability.
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HLA-B27-Associated Reactive Arthritis: Pathogenetic and Clinical Considerations

TL;DR: Current evidence supports the concept that reactive arthritis (ReA) is an immune-mediated synovitis resulting from slow bacterial infections and showing intra-articular persistence of viable, nonculturable bacteria and/or immunogenetic bacterial antigens synthesized by metabolically active bacteria residing in the joint and/ or elsewhere in the body.
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Telomerase insufficiency in rheumatoid arthritis

TL;DR: Restoring defective telomerase activity emerges as a therapeutic target in resetting immune abnormalities in RA and results in excessive T cell loss, undermining homeostatic control of the naive T cell compartment and setting the stage for lymphopenia-induced T cell repertoire remodeling.
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Deficiency of the DNA repair enzyme ATM in rheumatoid arthritis.

TL;DR: It is shown that ATM deficiency in RA disrupts DNA repair and renders T cells sensitive to apoptosis, and Restoration of DNA repair mechanisms emerges as an important therapeutic target in RA.
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An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies

TL;DR: An extensive simulation study built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data is presented to compare the performance of eight methods including the most commonly used approaches and recommends surrogate variable analysis for cell-type mixture adjustment.