Tea consumption and risk of lower respiratory tract infections: a two-sample mendelian randomization study.

BACKGROUND: Observational studies have reported the association between tea consumption and the risk of lower respiratory tract infections (LRTIs). However, a consensus has yet to be reached, and whether the observed association is driven by confounding factors or reverse causality remains unclear. METHOD: A two-sample Mendelian randomization (MR) analysis was conducted to determine whether genetically predicted tea intake is causally associated with the risk of common LRTI subtypes. Genome-wide association study (GWAS) from UK Biobank was used to identify single-nucleotide polymorphisms (SNPs) associated with an extra cup of tea intake each day. The summary statistics for acute bronchitis, acute bronchiolitis, bronchiectasis, pneumonia, and influenza and pneumonia were derived from the FinnGen project. RESULTS: We found that genetically predicted an extra daily cup of tea intake was causally associated with the decreased risk of bronchiectasis [odds ratio (OR) = 0.61, 95% confidence interval (CI) = 0.47-0.78, P < 0.001], pneumonia (OR = 0.90, 95% CI = 0.85-0.96, P = 0.002), influenza and pneumonia (OR = 0.91, 95% CI = 0.85-0.97, P = 0.002), but not with acute bronchitis (OR = 0.91, 95% CI = 0.82-1.01, P = 0.067) and acute bronchiolitis (OR = 0.79, 95% CI = 0.60-1.05, P = 0.100). Sensitivity analyses showed that no heterogeneity and pleiotropy could bias the results. CONCLUSIONS: Our findings provided new evidence that genetically predicted an extra daily cup of tea intake may causally associated with a decreased risk of bronchiectasis, pneumonia, and influenza and pneumonia.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer.

Objective: Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods: Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan-Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results: Univariate Cox regression and Kaplan-Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan-Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa (p < 0.05). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions: The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

Common mineral nutrients in ankylosing spondylitis: A 2-sample Mendelian randomization study.

OBJECTIVE: The causal relationship between common mineral nutrients and ankylosing spondylitis (AS) has not been studied. So this Mendelian randomization (MR) study aims to investigate the causal association of varying levels of calcium, zinc, copper, and selenium on AS. DESIGN: We selected 4 elements potentially associated with the onset and development of AS as exposure factors, single nucleotide polymorphisms (SNPs) as instrumental variables, and these SNPs are independent of each other(r2 < 0.05) and highly correlated with each of the 4 elements (P < 5 × 10-8 ). The 2-sample MR method takes Inverse-variance weighted (IVW) and MR-Egger as the main method and Simple mode (SM), Weighted median (WM1 ), and Weighted mode (WM2 ) as supplementary methods to evaluate the causal effect of mineral levels on AS. RESULTS: The IVW analysis does not provide convincing evidence to support a causal association between calcium (odds ratio [OR] = 1.000, 95% CI = 0.994, 1.005, P = .875), copper (OR = 1.000, 95% CI = 1.000, 1.001, P = .533) and selenium (OR = 0.999, 95% CI = 0.998, 1.000, P = .229) and AS. The IVW (OR = 1.001, 95% CI = 1.000, 1.002, P = .029) and WM1 (OR = 1.001, 95% CI = 1.000, 1.002, P = .011) results of zinc show that per standard deviation increment in zinc is a suggestive association with risks of AS, and MR-Egger (OR = 1.004, 95% CI = 0.996, 1.013, P = .265) and other supplementary methods indicate that zinc is not causally associated with AS. All MR-Egger intercept parameters and MR Pleiotropy RESidual Sum and Outlier tests demonstrated the absence of horizontal pleiotropy. CONCLUSIONS: This study does not provide convincing evidence to support a causal correlation between calcium, zinc, copper, and selenium with AS.

mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review.

AIMS: Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support. METHODS: A scoping review using the novel Survey Tool approach for collaborative literature Reviews (STaR) process was performed. RESULTS: From 18 papers, 11 discrete GDM-based mHealth apps were identified, but only 3 were reasonably mature with only one currently in use in a clinical setting. Two-thirds of the apps provided condition-relevant contextual user feedback that could aid in patient self care. However, although each app targeted one or more components of the GDM clinical pathway, no app addressed the entirety from diagnosis to postpartum. CONCLUSIONS: There are limited mHealth apps for GDM that incorporate AI or AI-based decision support. Many exist only to record patient information like blood glucose readings or diet, provide generic patient education or advice, or to reduce adverse events by providing medication or appointment alerts. Significant barriers remain that continue to limit the adoption of mHealth apps in clinical care settings. Further research and development are needed to deliver intelligent holistic mHealth apps using AI that can truly reduce healthcare resource use and improve outcomes by enabling patient self care in the community.

Water-Assisted/Water-Accelerated Photoreaction of trans-2,3,4',5-Tetrahydroxystilbene-2-O-ß-d-glucoside from the Roots of Polygonum multiflorum.

The photoreaction of 2,3,4',5-tetrahydroxystilbene-2-O-ß-d-glucoside (TSG) has been investigated. Water-assisted/water-accelerated photodimerization of trans-TSG favored the formation of syn-head-to-tail [2 + 2] photocyclobutane under 365 nm irradiation as a result of hydrophobic association and a fluorescent solute-solute aggregate from their excited singlet states. In contrast, irradiation with 254 nm led to [2 + 2] photocycloreversion. The two cyclobutane dimers were first obtained through straightforward photoreaction and identified as multiflorumiside A and multiflorumiside C through the detailed analysis of high-resolution electrospray ionization mass spectrometry and one- and two-dimensional nuclear magnetic resonance. Therefore, trans-TSG should be protected from light and water.

A Natural Product from Polygonum cuspidatum Sieb. Et Zucc. Promotes Tat-Dependent HIV Latency Reversal through Triggering P-TEFb's Release from 7SK snRNP.

The latent reservoirs of HIV represent a major impediment to eradication of HIV/AIDS. To overcome this problem, agents that can activate latent HIV proviruses have been actively sought after, as they can potentially be used in combination with the highly active antiretroviral therapy (HAART) to eliminate the latent reservoirs. Although several chemical compounds have been shown to activate latency, they are of limited use due to high toxicity and poor clinical outcomes. In an attempt to identify natural products as effective latency activators from traditional Chinese medicinal herbs that have long been widely used in human population, we have isolated procyanidin C-13,3',3"-tri-O-gallate (named as REJ-C1G3) from Polygonum cuspidatum Sieb. et Zucc., that can activate HIV in latently infected Jurkat T cells. REJ-C1G3 preferentially stimulates HIV transcription in a process that depends on the viral encoded Tat protein and acts synergistically with prostratin (an activator of the NF-κB pathway) or JQ1 (an inhibitor of Brd4) to activate HIV latency. Our mechanistic analyses further show that REJ-C1G3 accomplishes these tasks by inducing the release of P-TEFb, a host cofactor essential for Tat-activation of HIV transcription, from the cellular P-TEFb reservoir 7SK snRNP.

Cytotoxic 9,19-cycloartane triterpenes from the aerial parts of Cimicifuga yunnanensis.

Six new 9,19-cycloartane triterpenes (1-6) were isolated from the aerial parts of Cimicifuga yunnanensis. The new chemical structures were determined by extensive analyses of 1D and 2D NMR spectroscopy. Compounds 1 and 2 are the first 9,19-cycloartane triterpenes characterized by CH3-18 shifting from C-13 to C-12 in the Cimicifuga spp. The evaluation of inhibition activity against human HL-60, SMMC-7721, A-549, MCF-7, and SW480 cell lines indicated that compounds 1-6 showed different levels of cytotoxic activities with IC50 values ranging from 1.2 to 27.8 µm.