Prism graph pad 5

With a sample size of 30 (15 in each arm), this study had 80% power to detect a 0.25 difference in reduction of immune activation in the atorvastatin and placebo arm, with a statistical significance at P-value < 0.05. To assess the effects of treatment, we calculated the difference in changes, for immune activation and exhaustion parameters, while a participant was receiving atorvastatin or placebo. Pre- and post-treatment T-cell activation, percentage of activated T cells (CD3 + CD4 + (CD8)CD38 + HLADR+ cells) and exhaustion (CD3 + CD4 + (CD8)PD1 + ) were compared among SO-IR on atorvastatin (ATV) and placebo using the Mann–Whitney test for non-parametric tests. Additional analysis was carried out to determine carry-over and period effects using ANOVA test and the pk cross-STATA command. Given that there were no significant carry-over and period effects, results were presented for all the 30 participants for 12 weeks on atorvastatin vs. placebo. In addition, post-treatment immune activation and exhaustion were compared with the parameters among healthy donors from the same environment. Flow cytometry data were analysed using FlowJO software version and comparisons analysed using Prism Graph Pad 5.0 software and STATA version 11.0.

Statistical analysis was performed using Prism Graph Pad 5.0 (La Jolla, CA). One-way analysis of variance (ANOVA) was used to compare the NT₅₀ values to the chimeric virus and parental viruses at early convalescent, 3 and 18 months post-illness. Paired t test was used to compare the proportions of the DENV1 type-specific neutralizing response attributable to the 1F4 epitope between samples collected between 3 and 18 months post-infection. Statistical difference was considered significant when p-value was <0.05.

Imaging was conducted using an Olympus FV1000 laser scanning confocal microscope with the Fluoview software. Images were exported to ImageJ for analysis as described [1 (link)]. Synaptic puncta were imaged and quantified as described, with the following modification: the threshold was set automatically using the Otsu method [11 (link),23 (link)]. To differentiate whether double mutants exhibited a synergistic or additive phenotype, we used the following formula to estimate an additive model [Phenotype 1(%) + Phenotype 2(%) − (Phenotype 1(%) × Phenotype 2(%)]. Fisher’s exact test was calculated with Prism GraphPad (5.0) to determine whether observed values were significantly different from an additive model. In those calculations, we set a threshold of p < 0.005 to determine significance, to account for multiple testing. ANOVAs were performed in R [24 ] to determine if genotype, allele or animal stage contributed to the variance observed, comparing between double and triple mutants using R, with a Tukey honestly significant difference test to conduct pairwise comparisons. All genotypes were scored on a minimum of two different days, and the results were averaged between scoring sessions.

The experiment was performed using an established protocol (Shaffer and Rollins, 2020 (link)). Briefly, WT and ΔQ strains were grown till the mid-log phase and treated with 15 mM GO for 4 hr at 30 °C, and post-treatment cycloheximide was added to immobilize the ribosomes on mRNA. The cell lysates were prepared by adding lysis buffer (10 mM Tris pH 7.4, 100 mM NaCl, 30 mM MgCl₂, 100 µg/ml cycloheximide, 10 U RiboLock, and 1 mM PMSF) to the pellet and vortexed with glass beads. The lysates were loaded on SW41 tubes containing a 10% to 50% sucrose density gradient, followed by ultra-centrifugation. Later, the fractions were analyzed using a polysome profiler (Piston gradient fractionator; BIOCOMP). The area under the curve of polysome and monosome was calculated using Origin 8.0, and the ratio was plotted in Prism GraphPad 5.0.