Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue.

Rheumatoid arthritis (RA) is a complex immune-mediated inflammatory disorder in which patients suffer from inflammatory-erosive arthritis. Recent advances on histopathology heterogeneity of RA synovial tissue revealed three distinct phenotypes based on cellular composition (pauci-immune, diffuse and lymphoid), suggesting that distinct etiologies warrant specific targeted therapy which motivates a need for cost effective phenotyping tools in preclinical and clinical settings. To this end, we developed an automated multi-scale computational pathotyping (AMSCP) pipeline for both human and mouse synovial tissue with two distinct components that can be leveraged together or independently: (1) segmentation of different tissue types to characterize tissue-level changes, and (2) cell type classification within each tissue compartment that assesses change across disease states. Here, we demonstrate the efficacy, efficiency, and robustness of the AMSCP pipeline as well as the ability to discover novel phenotypes. Taken together, we find AMSCP to be a valuable cost-effective method for both pre-clinical and clinical research.

Differences in levels of stress, social support, health behaviours, and stress-reduction strategies for women pregnant before and during the COVID-19 pandemic, and based on phases of pandemic restrictions, in Ireland.

BACKGROUND: The COVID-19 pandemic and related restrictions can adversely impact antenatal maternal well-being and health behaviours. AIM: To examine antenatal stress and stress-reduction strategies, social support, and health behaviours between women pregnant before and during the pandemic in Ireland. METHODS: 210 pregnant women were recruited online and in the antenatal department of a tertiary maternity hospital before the pandemic, and 235 women recruited online during the pandemic. Only women resident in Ireland were included in this study. Women completed measures of stress, social support, health-behaviours, and self-reported stress-reduction strategies. Differences in outcomes were examined between women pregnant before and during the pandemic, and between Phase 2 and Phase 3 of the Irish Government COVID-19 restrictions. FINDINGS: Women pregnant during the pandemic reported lower perceived social support, including support from a significant other, friends and family, than women pregnant before the pandemic. There were no significant differences in stress in health behaviours but women reported higher stress and less physical activity during the pandemic. Women reported a range of comparable stress-reduction strategies before and during the pandemic. No differences were observed between phases of pandemic-related restrictions for any outcome. DISCUSSION: Our findings highlight negative impacts of the pandemic on social support, stress, and physical activity, which can have implications for maternal and child health. Lack of differences between restriction phases suggests on-going negative effects for antenatal well-being and behaviours. CONCLUSION: Development of supports for pregnant women during the pandemic should include social-support and stress-reduction components.

New Developments in Transcriptomic Analysis of Synovial Tissue.

Transcriptomic technologies are constantly changing and improving, resulting in an ever increasing understanding of gene expression in health and disease. These technologies have been used to investigate the pathological changes occurring in the joints of rheumatoid arthritis patients, leading to discoveries of disease mechanisms, and novel potential therapeutic targets. Microarrays were initially used on both whole tissue and cell subsets to investigate research questions, with bulk RNA sequencing allowing for further elaboration of these findings. A key example is the classification of pathotypes in rheumatoid arthritis using RNA sequencing that had previously been discovered using microarray and histology. Single-cell sequencing has now delivered a step change in understanding of the diversity and function of subpopulations of cells, in particular synovial fibroblasts. Future technologies, such as high resolution spatial transcriptomics, will enable step changes integrating single cell transcriptomic and geographic data to provide an integrated understanding of synovial pathology.