Standardization and other approaches to meta-analyze differences in means.

Meta-analysts often use standardized mean differences (SMD) to combine mean effects from studies in which the dependent variable has been measured with different instruments or scales. In this tutorial we show how the SMD is properly calculated as the difference in means divided by a between-subject reference-group, control-group, or pooled pre-intervention SD, usually free of measurement error. When combining mean effects from controlled trials and crossovers, most meta-analysts have divided by either the pooled SD of change scores, the pooled SD of post-intervention scores, or the pooled SD of pre- and post-intervention scores, resulting in SMDs that are biased and difficult to interpret. The frequent use of such inappropriate standardizing SDs by meta-analysts in three medical journals we surveyed is due to misleading advice in peer-reviewed publications and meta-analysis packages. Even with an appropriate standardizing SD, meta-analysis of SMDs increases heterogeneity artifactually via differences in the standardizing SD between settings. Furthermore, the usual magnitude thresholds for standardized mean effects are not thresholds for clinically important differences. We therefore explain how to use other approaches to combining mean effects of disparate measures: log transformation of factor effects (response ratios) and of percent effects converted to factors; rescaling of psychometrics to percent of maximum range; and rescaling with minimum clinically important differences. In the absence of clinically important differences, we explain how standardization after meta-analysis with appropriately transformed or rescaled pre-intervention SDs can be used to assess magnitudes of a meta-analyzed mean effect in different settings.

Systematic Review and Meta-analysis of the Relationship Between Proton Pump Inhibitors and Esophageal Adenocarcinoma.

INTRODUCTION AND AIM: Esophageal adenocarcinoma (EAC) mortality continues to increase across the world. This meta-analysis was aimed to investigate the relationship between proton pump inhibitors (PPIs) and the risk of EAC. METHODS: This meta-analysis was done as per the PRISMA checklist using relevant keywords. To this end, an extensive search was done on 29/6/2022 in EMBASE, Web of Science (ISI), PubMed, and Scopus. In this study, 95% confidence interval (CI) and standardized mean difference (SMD) were used to estimate the overall effect size. Analysis of the odds ratio (OR) for EAC was done using a random effects model. RESULTS: A total of 20 studies were included in the review. Compared to the group that received PPIs, the OR of EAC in the recipients of the PPIs group was obtained at 0.67 (95% CI = 0.39-1.29, P = 0.240). The meta-regression, including year, follow-up time, study design, sample size, quality of the study, study period, and geographical location, demonstrated no source of heterogeneity (P > 0.10). CONCLUSION: No significant relationship was found between PPIs use and the risk of EAC. Accordingly, PPIs do not have a protective or risk factor effect on EAC.

Production of glutaric acid from 5-aminovaleric acid using Escherichia coli whole cell bio-catalyst overexpressing GabTD from Bacillus subtilis.

Glutaric acid is one of the promising C5 platform compounds in the biochemical industry. It can be produced chemically, through the ring-opening of butyrolactone followed by hydrolysis. Alternatively, glutaric acid can be produced via lysine degradation pathways by microorganisms. In microorganisms, the overexpression of enzymes involved in this pathway from E. coli and C. glutamicum has resulted in high accumulation of 5-aminovaleric acid. However, the conversion from 5-aminovaleric acid to glutaric acid has resulted in a relatively low conversion yield for unknown reasons. In this study, as a solution to improve the production of glutaric acid, we introduced gabTD genes from B. subtilis to E. coli for a whole cell biocatalytic approach. This approach enabled us to determine the effect of co-factors on reaction and to achieve a high conversion yield from 5-aminovaleric acid at the optimized reaction condition. Optimization of whole cell reaction by different plasmids, pH, temperature, substrate concentration, and cofactor concentration achieved full conversion with 100 mM of 5-aminovaleric acid to glutaric acid. Nicotinamide adenine dinucleotide phosphate (NAD(P)+) and α-ketoglutaric acid were found to be critical factors in the enhancement of conversion in selected conditions. Whole cell reaction with a higher concentration of substrates gave 141 mM of glutaric acid from 300 mM 5-aminovaleric acid, 150 mM α-ketoglutaric acid, and 60 mM NAD+ at 30 °C, with a pH of 8.5 within 24 h (47.1% and 94.2% of conversion based on 5-aminovaleric acid and α-ketoglutaric acid, respectively). The whole cell biocatalyst was recycled 5 times with the addition of substrates; this enabled the accumulation of extra glutaric acid.