Spss statistical analysis software

Sample size was calculated based on change in HTGC from previous data in NAFLD¹² (link); an 80% power of detecting a 10% relative difference between group change in liver HTGC with a SD of 9.0 and a 1-sided 0.05 significance required n = 11 per group. We recruited 13 per sample to allow 2 drop-outs per group. Normality was assessed using a Kolmogorov–Smirnov test and logarithmically transformed if not normally distributed. Between-group differences were evaluated using an unpaired t test and within-group differences using a paired t test (2 way). Treatment × time interactions were assessed using a 2-way analysis of variance. Analyses of covariance were used to test for between-group differences in outcome variables while controlling for baseline values. Bivariate correlations using Pearson rank correlations were conducted to investigate any associations between HTGC, body composition, triglyceride levels, glucose control, biomarkers of inflammation, and NAFLD fibrosis marking systems. Statistical significance was set at a P value of less than .05. Statistical analyses were performed using SPSS statistical analysis software (version 19; IBM, Chicago, Illinois). All authors had access to the study data and reviewed and approved the final manuscript.

Participant demographics and symptom profiles were compared to those of the wider hospital (requested via clinical information services), routinely collected national child and adolescent mental health service (CAMHS) outcome data from the Child Outcomes Research Consortium (CORC) dataset [46 (link)] and data from a national initiative to improve children’s access to evidence-based psychological therapies (CYP IAPT) [23 (link),36 (link),47 ] by running chi-square tests of homogeneity using R statistical software, version 3.6.3 (R Project for Statistical Computing). Post hoc analyses involving pairwise comparisons using multiple z-tests of two proportions with Bonferroni correction was applied where chi-square tests were statistically significant (p < 0.05). As the amount of clustered data was small, with only six families of those allocated to an intervention containing more than one participant, we accounted for clustering by removing those six families from the analysis. All descriptive statistics were undertaken using SPSS statistical analysis software (version 25, IBM).

All statistical analyses were performed using an unpaired Student’s t test and the IBM SPSS statistical analysis software (version 23.0). All values are expressed as the mean ± standard error of the mean (SEM). Sample size was determined based on the previous studies with similar experiments (n number noted in the specific figure legends).

All the statistical analyses were performed using SPSS statistical analysis software (ver. 24.0, IBM, Armonk, NY, USA) with a confidence level of 95% (p < 0.05). The in vitro and in vivo results were analyzed by using the independent student’s t-test to determine the significance of differences between groups.

The fingerprint similarity of Berberidis Cortex was evaluated by Similarity Evaluation System for the chromatographic fingerprint of TCMs (version 2012, Chinese Pharmacopoeia Committee). The standardized fingerprint chromatograms were obtained based on the calibration and normalization of the common peak in ten batches of Berberidis Cortex SIMCA (14.1 Version, Umetrics, Sweden) multivariate statistical analysis software was used for Principal component analysis (PCA) by common peak area in ten batches. Taking the peak area as variables, the clustering analysis (CA) was calculated by SPSS statistical analysis software (21.0 Version, IBM Corp, United States) and GraphPad Prism (6.01 Version) for sample classification. Besides, verification of the accuracy and reliability of QAMS by comparing with ESM was performed to determine the other four active components in samples.

To test the frequency of novel c.398delC variant, two groups made up by 300 and 600 subjects, respectively, heterogeneous for age and gender, were collected. The first one was recruited in Fiumedinisi and sampling criteria include Fiumedinisi native ancestors by at least three generations, absence of consanguinity, and absence of inherited disabling ocular diseases. The second group was collected in the Sicilian area: Ionic and Tyrrhenian municipality in province of Messina, like Santa Teresa Riva, Roccalumera, Pace del Mela, were excluded due to their geographic localization, since destinations of the inhabitants of Fiumedinisi who have left their native town. For each population, the c.398delC allele frequency was calculated as [1 × (h + 2H)]/2 N, where h represents the heterozygous genotype, H thehomozygous genotype, and N the sample size for each population. Deviation from Hardy-Weinberg equilibrium (HWE) was determined using the χ²test with 2 × 3 contingency tables and 1 degree of freedom. Analysis was performed by the IBM SPSS statistical analysis software.

All experiments were repeated three times and performed in triplicate. Data were analyzed with SPSS statistical analysis software (version 22.0, IBM Corp., Armonk, NY, USA) using the probit analysis statistical method. The LC₅₀ values (with 95% confidence limits) were calculated. Differences among the results were considered to be statistically significant when the p value was <0.05. MS Excel 2007 was used to determine the regression equation (Y = mortality; X = concentrations), and the LC₅₀ was derived from the obtained best-fit line. One-way ANOVA followed by post hoc Tukey test and two-way ANOVA followed by Duncan’s test and t test were applied to determine significant differences in teratogenicity, behavior, learning impairment assessment, and gene expression between exposed and control groups. Data are presented as mean values ± standard error of the mean (SEM), with significant differences relative to the control (p-values ≤ 0.05). GraphPad Prism statistical software (GraphPad Software, San Diego, CA, USA) was used for all graphs.