METHODS: Information regarding the consumption of coffee, tea, and alcohol was collected from the UK Biobank, with sample sizes of 428,860, 447,485, and 462,346 individuals, respectively. Data on 41 inflammatory cytokines were obtained from summary statistics of 8293 healthy participants from Finnish cohorts.
RESULTS: The consumption of coffee was found to be potentially associated with decreased levels of Macrophage colony-stimulating factor (β = -0.57, 95% CI -1.06 ~ -0.08; p = 0.022) and Stem cell growth factor beta (β = -0.64, 95% CI -1.16 ~ -0.12; p = 0.016), as well as an increase in TNF-related apoptosis-inducing ligand (β = 0.43, 95% CI 0.06 ~ 0.8; p = 0.023) levels. Conversely, tea intake was potentially correlated with a reduction in Interleukin-8 (β = -0.45, 95% CI -0.9 ~ 0; p = 0.045) levels. Moreover, our results indicated an association between alcohol consumption and decreased levels of Regulated on Activation, Normal T Cell Expressed and Secreted (β = -0.24, 95% CI -0.48 ~ 0; p = 0.047), as well as an increase in Stem cell factor (β = 0.17, 95% CI 0.02 ~ 0.31; p = 0.023) and Stromal cell-derived factor-1 alpha (β = 0.20, 95% CI 0.04 ~ 0.36; p = 0.013).
CONCLUSION: Revealing the interactions between beverage consumption and various inflammatory cytokines may lead to the discovery of novel therapeutic targets, thereby facilitating dietary interventions to complement clinical disease treatments.
METHODS: Cytokines were measured using a commercial Bio-plex Pro Human Cytokine Grp I Panel 17-plex kit (BioRad, Hercules, CA, USA). Inflammation was assessed by measuring an array of plasma cytokines, and phenotypic alterations in CD4+ T cells including circulating Tfh cells, CD8+ T cells, and TCR iVα7.2+ MAIT cells in chronic HBV, HCV, and HIV-infected patients and healthy controls. The cells were characterized based on markers pertaining to immune activation (CD69, ICOS, and CD27) proliferation (Ki67), cytokine production (TNF-α, IFN-γ) and exhaustion (PD-1). The cytokine levels and T cell phenotypes together with cell markers were correlated with surrogate markers of disease progression.
RESULTS: The activation marker CD69 was significantly increased in CD4+hi T cells, while CD8+ MAIT cells producing IFN-γ were significantly increased in chronic HBV, HCV and HIV infections. Six cell phenotypes, viz., TNF-α+CD4+lo T cells, CD69+CD8+ T cells, CD69+CD4+ MAIT cells, PD-1+CD4+hi T cells, PD-1+CD8+ T cells, and Ki67+CD4+ MAIT cells, were independently associated with decelerating the plasma viral load (PVL). TNF-α levels showed a positive correlation with increase in cytokine levels and decrease in PVL.
CONCLUSION: Chronic viral infection negatively impacts the quality of peripheral MAIT cells and Tfh cells via differential expression of both activating and inhibitory receptors.
METHODS: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals.
RESULTS: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences.
CONCLUSIONS: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.