Microsoft excel 2019

Statistical analyses were performed using Microsoft Excel 2019 and SPSS version 17.0 (SPSS Inc., Chicago, IL, United States). Descriptive statistics are presented as the mean ± SD for continuous variables and n (%) for categorical variables. We conducted a Pearson correlation analysis to assess the correlations between continuous independent variables and the total PSQI score. The t-test and analysis of variance (ANOVA) were used for univariate analysis of categorical predictors. All predictors were then subjected to multiple regression analysis with forced entry and stepwise entry methods to examine their independent effects on PSQI. Results yielding a value of p < 0.05 were considered statistically significant.

Normality of data distribution was assessed via measures of skewness and kurtosis. All continuous variables are expressed as means ± standard deviation and categorical data are expressed as frequencies and percentages. Participants were grouped based on DII score into pro- or anti-inflammatory dietary groups. Independent samples t-test were used to compare participant characteristic data and sarcopenia symptomology (muscle quantity, muscle strength and muscle performance) between DII groups. Pearson correlation coefficients (or Spearman’s rho for non-parametric data) were used to explore a correlation between DII, ASM, HGS, STS, and TUG. Multiple linear regression analyses were used to explore associations between DII and sarcopenia symptomology controlling for the following covariates: age, gender, comorbidities, waist circumference and physical activity levels. All data was analysed using Microsoft Excel 2019 and SPSS (version 22.0, SPSS, Inc., Chicago, IL, USA) with significance set at p < 0.05.

The data were processed using Microsoft Excel 2019, SPSS software (version 27.0, SPSS Inc., Chicago, IL, USA), and GraphPad Prism (version 9.5.1). R software (version 3.0) was used to draw the figures. Differences between the WVT and NVT groups were calculated using the χ² test or Fisher’s exact test. Survival was analyzed using Kaplan–Meier curves, the log-rank test, and Cox regression. ROC curves were utilized to filter out microbiome biomarkers for different groups (HCC vs LC and WVT vs NVT), and the AUC was commonly used to assess discriminative power. An AUC value close to 1 (AUC > 0.8) indicated good classifier variables. For correlation analysis between microbial signatures and clinical characteristics, Spearman’s correlation was calculated and performed on the R software package of “ggplot2,” “RColorBrewer,” and “reshape2.” R coefficient was calculated to measure the relationship between microbial signatures and clinical characteristics, and R coefficient was divided into three types (week: 0.2–0.4, medium: 0.4–0.6, and strong: >0.6). Statistical parameters, including the exact value of n and statistical significance (P-value), are shown in the Figure Legends. Statistical significance was set at a two-sided P-value < 0.05.

R software (v3.6.3.), Microsoft Excel 2019, and SPSS (IBM SPSS Statistics 21, Inc., Chicago, IL, United States) were used for descriptive statistical analysis and plotting graphs. Curve fitting of the annual number of publications and citations was performed using Microsoft Excel. The best fitting model was selected based on the highest correlation coefficient (R²). We calculated the growth rate of publications according to the specific calculation formula described by Wu et al. (Wu et al., 2021a (link)). To test correlation between publications and citations, Pearson’s correlation coefficient was calculated and correlations were considered significant with a p value less than 0.05.