Micro-RNA mediated regulation of a cytokine factor: TNF-alpha: an exploration of gene expression control in proliferating and quiescent cells

Two types mechanisms that control gene expression involve cis-regulatory factors and trans-regulatory factors. Cis-acting regulatory RNAs include targeted messenger RNA (mRNA) specificity and AU-rich elements (AREs). AU-rich mRNAs are a subcategory of mRNAs that have AREs in their 3'-Untranslated Regions (UTRs). These ARE-genes have been observed to correlate with rapid mRNA decay patterns. They comprise approximately 12% of all transcripts and are known to encode for a group of proteins that have involvement in the inflammatory response. Trans-acting regulatory mechanisms are micro RNAs (miRNAs) in eukaryotes, and small RNAs (sRNA) in prokaryotes. Misregulation of these mechanisms can lead to many disease states if rapid mRNA decay does not occur, leading to tumorigenesis, and eventually, different types of cancer. In this project, the TNF-α ARE was studied in both serum-positive and quiescent G0 conditions in order to analyze whether the translation of the gene differed in any respect due to the binding of a known miRNA called miR-130a. Additionally, both serum-positive and one-day serum-starved quiescent G0 conditions were analyzed for eIF5B and FXR1 levels to analyze whether there was a correlation between the two proteins

Safety and efficacy of hydrothermal duodenal mucosal resurfacing in patients with type 2 diabetes: the randomised, double-blind, sham-controlled, multicentre REVITA-2 feasibility trial

Objective: Hydrothermal duodenal mucosal resurfacing (DMR) is a safe, outpatient endoscopic procedure. REVITA-2, a double-blind, superiority RCT, investigates safety and efficacy of DMR using the single catheter Revita system (Revita DMR [catheter and system], on glycaemic control and liver fat content in Type 2 Diabetes (T2D).Design: Eligible patients (HbA1c 59–86mmol/mol, BMI ≥24 and ≤40kg/m2, fasting insulin >48.6pmol/L, ≥1 oral antidiabetic medication) enrolled in Europe and Brazil. Primary endpoints were safety, change from baseline in HbA1c at 24 weeks, and liver magnetic resonance imaging proton-density fat fraction (MRI-PDFF) at 12 weeks. Results: Overall mITT (DMR N=56; sham N=52), 24-weeks post-DMR, median (IQR) HbA1c change was −10.4 (18.6) mmol/mol in DMR group versus −7.1 (16.4) mmol/mol in sham group (p=0.147). In patients with baseline liver MRI-PDFF >5% (DMR n=48; sham n=43), 12-week post-DMR liver-fat change was −5.4 (5.6)% in DMR group versus −2.9 (6.2)% in sham group (p=0.096). Results from prespecified interaction testing and clinical parameter assessment showed heterogeneity between European (DMR N=39; sham N=37) and Brazilian (DMR N=17; sham N=16) populations (p=0.063), therefore, results were stratified by region. In European mITT, 24-weeks post-DMR, median (IQR) HbA1c change was –6.6 mmol/mol (17.5 mmol/mol) versus –3.3 mmol/mol (10.9 mmol/mol) post-sham (p=0.033); 12-week post-DMR liver-fat change was –5.4% (6.1%) versus –2.2% (4.3%) post-sham (p=0.035). Brazilian mITT results trended towards DMR benefit in HbA1c, but not liver fat, in context of a large sham effect. In overall PP, patients with high baseline fasting plasma glucose ([FPG] ≥10 mmol/L) had significantly greater reductions in HbA1c post-DMR versus sham (p=0.002). Most adverse events were mild and transient. Conclusions: DMR is safe and exerts beneficial disease-modifying metabolic effects in T2D with or without non-alcoholic liver disease (NAFLD), particularly in patients with high FPG

County community health associations of net voting shift in the 2016 U.S. presidential election

Importance In the U.S. presidential election of 2016, substantial shift in voting patterns occurred relative to previous elections. Although this shift has been associated with both education and race, the extent to which this shift was related to public health status is unclear. Objective: To determine the extent to which county community health was associated with changes in voting between the presidential elections of 2016 and 2012. Design: Ecological study with principal component analysis (PCA) using principal axis method to extract the components, then generalized linear regression. Setting: General community. Participants: All counties in the United States. Exposures Physically unhealthy days, mentally unhealthy days, percent food insecure, teen birth rate, primary care physician visit rate, age-adjusted mortality rate, violent crime rate, average health care costs, percent diabetic, and percent overweight or obese. Main outcome The percentage of Donald Trump votes in 2016 minus percentage of Mitt Romney votes in 2012 (“net voting shift”). Results: Complete public health data was available for 3,009 counties which were included in the analysis. The mean net voting shift was 5.4% (+/- 5.8%). Of these 3,009 counties, 2,641 (87.8%) had positive net voting shift (shifted towards Trump) and 368 counties (12.2%) had negative net voting shift (shifted away from Trump). The first principal component (“unhealthy score”) accounted for 68% of the total variance in the data. The unhealthy score included all health variables except primary care physician rate, violent crime rate, and health care costs. The mean unhealthy score for counties was 0.39 (SD 0.16). Higher normalized unhealthy score was associated with positive net voting shift (22.1% shift per unit unhealthy, p < 0.0001). This association was stronger in states that switched Electoral College votes from 2012 to 2016 than in other states (5.9% per unit unhealthy, p <0.0001). Conclusions and relevance Substantial association exists between a shift toward voting for Donald Trump in 2016 relative to Mitt Romney in 2012 and measures of poor public health. Although these results do not demonstrate causality, these results suggest a possible role for health status in political choices

Baseline demographics of county-level data^*.

<p>Baseline demographics of county-level data^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185051#t001fn001" target="_blank">*</a>}.</p

Generalized linear regression estimates adjusted for demographic variables and unhealthy component.

<p>Generalized linear regression estimates adjusted for demographic variables and unhealthy component.</p

Interaction of unhealthy factor with region.

<p>Interaction of unhealthy factor with region.</p

Longitudinal Change in Galectin-3 and Incident Cardiovascular Outcomes

BACKGROUND Galectin-3 (Gal-3) has been associated with heart failure (HF) and poor cardiovascular outcomes. However, the effect of longitudinal changes in Gal-3 on clinical outcomes remains unclear. OBJECTIVES The authors sought to study clinical determinants of change in Gal-3 among community-dwelling individuals. Further, they sought to examine the role of serial Gal-3 measurements in predicting risk of future HF, cardiovascular disease (CVD), and mortality. METHODS A total of 2,477 participants in the Framingham Heart Study Offspring cohort underwent measurement of plasma Gal-3 levels at 2 examinations (1995 to 1998 and 2005 to 2008). Linear regression models were used to examine clinical correlates of change in Gal-3. Proportional hazards models were used to relate future clinical outcomes with change in Gal-3. RESULTS The following clinical correlates were associated with greater longitudinal increases in Gal-3 levels: age, female sex, hypertension, diabetes, body mass index, interim development of chronic kidney disease, and HF (p <0.0001 for all in multivariable model). Change in Gal-3 was associated with future HF (hazard ratio [HR]: 1.39 per 1-SD increase; 95% confidence interval [CI]: 1.13 to 1.71), CVD (HR: 1.29; 95% CI: 1.11 to 1.51), and all-cause mortality (HR: 1.30; 95% CI: 1.17 to 1.46). Change in Gal-3 was associated with both HF with preserved as well as reduced ejection fraction (p <0.05 for both). CONCLUSIONS Longitudinal changes in Gal-3 are associated with traditional cardiovascular risk factors and renal disease. In turn, change in Gal-3 predicts future HF, CVD, and mortality in the community. Future studies are needed to determine whether serial Gal-3 measures may be useful in disease prevention. (J Am Coll Cardiol 2018; 72: 3246-54) (c) 2018 by the American College of Cardiology Foundation