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- Johnson, Wade T, et al.
(författare)
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Immunomodulatory Nanoparticles for Modulating Arthritis Flares
- 2023
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Ingår i: ACS nano. - 1936-086X. ; 18:3, s. 1892-1906
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Tidskriftsartikel (refereegranskat)abstract
- Disease-modifying drugs have improved the treatment for autoimmune joint disorders, such as rheumatoid arthritis, but inflammatory flares are a common experience. This work reports the development and application of flare-modulating poly(lactic-co-glycolic acid)-poly(ethylene glycol)-maleimide (PLGA-PEG-MAL)-based nanoparticles conjugated with joint-relevant peptide antigens, aggrecan70-84 and type 2 bovine collagen256-270. Peptide-conjugated PLGA-PEG-MAL nanoparticles encapsulated calcitriol, which acted as an immunoregulatory agent, and were termed calcitriol-loaded nanoparticles (CLNP). CLNP had a ∼200 nm hydrodynamic diameter with a low polydispersity index. In vitro, CLNP induced phenotypic changes in bone marrow derived dendritic cells (DC), reducing the expression of costimulatory and major histocompatibility complex class II molecules, and proinflammatory cytokines. Bulk RNA sequencing of DC showed that CLNP enhanced expression of Ctla4, a gene associated with downregulation of immune responses. In vivo, CLNP accumulated in the proximal lymph nodes after intramuscular injection. Administration of CLNP was not associated with changes in peripheral blood cell numbers or cytokine levels. In the collagen-induced arthritis and SKG mouse models of autoimmune joint disorders, CLNP reduced clinical scores, prevented bone erosion, and preserved cartilage proteoglycan, as assessed by high-resolution microcomputed tomography and histomorphometry analysis. The disease protective effects were associated with increased CTLA-4 expression in joint-localized DC and CD4+ T cells but without generalized suppression of T cell-dependent immune response. The results support the potential of CLNP as modulators of disease flares in autoimmune arthropathies.
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| 2. |
- Mehta, Raghav, et al.
(författare)
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QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
- 2022
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Ingår i: Journal of Machine Learning for Biomedical Imaging. - 2766-905X. ; , s. 1-54
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
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