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We determined differences between multiple groups using one-way analysis of variance, with Tukey multiple comparison post-test comparisons.

Comparisons were made between multiple groups using ANOVA and the unpaired student t-test as a post-hoc test.

Continuous variables were compared between multiple groups using analysis of variance (ANOVA) while categorical variables were compared using the X test.

Averaged data are reported as the mean ± the standard error of the mean (s.e.m .. Comparison for two groups was performed using a Student's t-test and between multiple groups using Kruskal-Wallis one-way ANonA on ranks for nonparametric data.

Statistical comparisons between two groups were conducted by using the Student's t-test and between multiple groups using two-way ANOVA using the (Prism software GraphPad Software, Inc., La Jolla, CA, USA).

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Comparisons between multiple groups used Kruskal-Wallis tests with Bonferroni correction of post hoc Mann–Whitney U tests.

For comparison of data between multiple groups we used Kruskal-Wallis test and for comparison between two groups we used Mann-Whitney U test.

Since multiple measurements were obtained from each sIA, Friedman test for non-parametric testing of related samples between multiple groups was used for statistical comparison of nuclear densities and staining patterns in Oil-Red-O or copper oxidized LDL immunostainings.

For comparison of data between multiple groups, we used one-way analysis of variance (ANOVA) with post-hoc Dunnett's test and Kruskal Wallis one-way ANOVA as appropriate.

The mean values of two independent samples were compared using t-test; comparison between multiple groups was analyzed using one-way ANOVA, while pairwise comparisons between groups were analyzed using LSD- t test.

All data shown are means±S.E.M. Statistical difference between percentages of stained cells was measured using univariate t-test for means and comparisons between multiple groups were analyzed using ANOVA and post hoc comparison using Tukey's test for paired comparisons.

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