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1.
Article in English | WPRIM (Western Pacific) | ID: wpr-999968

ABSTRACT

Background/Aims@#Immune and inflammatory cells respond to multiple pathological hits in the development of nonalcoholic steatohepatitis (NASH) and fibrosis. Relatively little is known about how their type and function change through the non-alcoholic fatty liver disease (NAFLD) spectrum. Here we used multi-dimensional mass cytometry and a tailored bioinformatic approach to study circulating immune cells sampled from healthy individuals and people with NAFLD. @*Methods@#Cytometry by time of flight using 36 metal-conjugated antibodies was applied to peripheral blood mononuclear cells (PBMCs) from biopsy-proven NASH fibrosis (late disease), steatosis (early disease), and healthy patients. Supervised and unsupervised analyses were used, findings confirmed, and mechanisms assessed using independent healthy and disease PBMC samples. @*Results@#Of 36 PBMC clusters, 21 changed between controls and disease samples. Significant differences were observed between diseases stages with changes in T cells and myeloid cells throughout disease and B cell changes in late stages. Semi-supervised gating and re-clustering showed that disease stages were associated with fewer monocytes with active signalling and more inactive NK cells; B and T cells bearing activation markers were reduced in late stages, while B cells bearing co-stimulatory molecules were increased. Functionally, disease states were associated with fewer activated mucosal-associated invariant T cells and reduced toll-like receptor-mediated cytokine production in late disease. @*Conclusions@#A range of innate and adaptive immune changes begin early in NAFLD, and disease stages are associated with a functionally less active phenotype compared to controls. Further study of the immune response in NAFLD spectrum may give insight into mechanisms of disease with potential clinical application.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-20248155

ABSTRACT

ObjectivesOnline health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users. Setting and designWe obtained data from 739,434 posts by 53,134 unique users of three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1st January 2020 to 31st May 2020. Using NLP, we analysed the content of posts related to COVID-19. Primary outcome measuresO_LIProportion of forum posts containing COVID-19 keywords C_LIO_LIProportion of forum users making their very first post about COVID-19 C_LIO_LINumber of COVID-19 related posts containing content related to physical and mental health comorbidities C_LI ResultsPosts discussing COVID-19 and related comorbid disorders spiked in early- to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. The pandemic and corresponding public response has had a significant impact on posters queries regarding mental health. ConclusionsWe demonstrate it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance. Article SummaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIAnalysing online health forum data using NLP revealed a substantial rise in activity which correlated with the onset of the COVID-19 pandemic. C_LIO_LIReal-time data sources such as online health forums are essential for monitoring fluctuating population health and tailoring responses to daily pressures. C_LIO_LIIt is not yet possible to establish COVID-19 status or whether concerned posters have pre-existing mental or physical health issues, are recovered, or have become unwell for the first time. C_LIO_LIOnline health forums are help-seeking forums, which introduces self-selection bias. C_LI

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