Functional foods and nutrition have become increasingly popular in preventing and reducing the incidence of diabetes. Green coffee bean extract (GCBE) has received much interest because of the evidence that coffee consumption reduces the risk of diabetes and many inflammatory diseases. This study was designed to investigate the phytochemicals contained in GCBE and their antioxidant, anti-diabetic, and anti-inflammatory efficacies. GCBE phytochemicals were analyzed using high-performance liquid chromatography (HPLC). This analysis demonstrated that chlorogenic acid was the predominant component of GCBE, followed by caffeine and caffeic acid. The antioxidant capacity of GCBE was assessed using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2’-azinobis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays, demonstrating significant scavenging capacity with IC50 values of 2.96 ± 1.04 and 7.63 ± 1.03 µg/mL, respectively. The anti-hyperlipidemic efficacy of GCBE was observed through inhibiting cholesterol absorption (by increasing micelle sizes and decreasing cholesterol solubility), lipid digestion, and pancreatic lipase activity in vitro. The investigations revealed that GCBE possessed anti-hyperlipidemic properties by inhibiting cholesterol absorption, lipid digestion, and pancreatic lipase activity. Specifically, GCBE increased micelle particle sizes by ~6.5-fold, decreased cholesterol solubility by 2-fold, and reduced pancreatic lipase activity by 25%. Additionally, the in vitroanti-hyperglycemic activity of GCBE was evaluated by inhibition of α-amylase and α-glucosidase capacity. GCBE demonstrated anti-hyperglycemic activity by inhibiting α-amylase activity (32.80 ± 7.06% inhibition), while α-glucosidase activity remained unaffected. The anti-inflammatory potential of GCBE was evaluated by mRNA regulation using RT-PCR analysis. This analysis revealed that GCBE attenuated mRNA expression of COX-2, TNF-α, IL-1b, and IL-6 in LPS-induced RAW264.7 cells. GCBE’s antioxidant, anti-hyperlipidemic, and anti-hyperglycemic efficacies and its molecular mechanisms in modulating the inflammation pathway found in the present study highlight its potential as a supplement in functional foods or beverages. Doi: 10.28991/HEF-2024-05-01-08 Full Text: PDF

Data on natural rainfall and fish density often exhibit positively right-skewed distributions and include zero observations, thus making them fit a lognormal model with zero inflation. Estimation methods for the variance of a lognormal model with zero inflation using the modified Fletcher (MFL) interval, the generalized confidence interval, and the method of variance estimate recovery are derived herein. The coverage rates and average widths of the proposed methods were assessed via Monte Carlo simulation. Numerical results reveal that the MFL satisfied the requirements for handling large variances, even in situations where the proportion of zero data items was high. Natural rainfall and fish density and data are used to illustrate the efficacies of the proposed methods for estimating rainfall and fish dispersion, respectively.

The coefficient of quartile variation is a valuable measure used to assess data dispersion when it deviates from a normal distribution or displays skewness. In this study, we focus specifically on the delta-lognormal distribution. The lognormal distribution is characterized by its asymmetrical nature and comprises exclusively positive values. However, when these values undergo a logarithmic transformation, they conform to a symmetrical (or normal) distribution. Consequently, this research aims to establish confidence intervals for the difference between coefficients of quartile variation within lognormal distributions incorporating zero values. We employ the Bayesian, generalized confidence interval, and fiducial generalized confidence interval methods to construct these intervals, involving data simulation using RStudio software. We evaluate the performance of these methods based on coverage probabilities and average lengths. Our findings indicate that the Bayesian method, employing Jeffreys’ prior, performs well in low variability, while the generalized confidence interval method is more suitable for higher variability. Therefore, we recommend using both approaches to construct confidence intervals for the difference between the coefficients of the quartile variation in lognormal distributions that include zero values. Furthermore, we apply these methods to rainfall data in Thailand to illustrate their alignment with actual and simulated data.

A reliable interval estimation of the latent mean incidence rate is important but challenging in infectious disease surveillance because count data are often zero-inflated and overdispersed. This study compared frequentist (normal approximation (NA), bootstrap percentile (BP), and bootstrap-t (BT)) and Bayesian (equal-tailed interval (ETI) and highest density interval (HDI)) intervals under the zero-inflated negative binomial (ZINB) model. A Monte Carlo simulation study varied levels of zero inflation, dispersion, and the observation period, evaluating conditional and unconditional coverage and average interval length. NA and BP showed undercoverage throughout the evaluated scenarios. BT improved conditional coverage over BP in 47 of 54 scenarios but generally produced wider intervals. Bayesian intervals often had higher coverage, although overcoverage and diagnostic failures prevented a uniform advantage. HDI was shorter than ETI in every scenario. An empirical application to dengue surveillance data from Phayao Province, Thailand, illustrated the five interval procedures under a fitted ZINB model; however, the NB model had lower AIC/BIC values. Temporal diagnostics and block bootstrap results indicated sensitivity to dependence assumptions. Overall, interval performance depended on the scenario, prior specification, and successful interval construction; no method uniformly achieved nominal coverage.