Spss software for windows version 23

Continuous variables with normal distribution are represented as mean ± standard deviation, while those without the normal distribution are represented as median (interquartile range). Categorical classified variables are represented as numbers (percentage). Primary descriptive statistics of the study were reported. Incidence of malignant NMLs was calculated. Because the irregular indetermined shape, ambiguous contour and blurring margin, the size of breast NMLs was difficult to measure, and therefore it was not calculated. Odds ratio (OR) was determined for the variable analysis. Independent sample t-test was used for the analyses of continuous variables. The receiver operating characteristic (ROC) curve was drawn, the area under the ROC curve (AUC) was calculated, and Youden index was determined and taken as the cut-off value for the determination of malignancy risk stratification of the sensitivity and specificity. The positive predictive value(PPV) and negative predictive value(NPV) were calculated. All analyses were conducted using SPSS software for Windows, version 23 (IBM Corp, Armonk, NY, USA) and/or Medcalc statistical software version 15.2.2 (Medcalc software BVBA, Ostend, Belgium), and a p<0.05 was considered statistically significant.

The results were expressed as frequency and percentage for categorical variables, and as mean ± standard deviation (SD) or median (interquartile range [IQR]) for continuous variables. For continuous variables, comparisons between groups were carried out using Student’s t-test or the Mann–Whitney U test, as well as one-way analysis of variance (ANOVA). The percentage of patients with a given characteristic in each group was compared using the Chi-square test, or Fisher’s exact test in the case of small numbers. Logistic regressions were used to investigate the association between plasma BDNF and TSPO categories and having AIS, after adjusting the confounding factors. The odds ratio (OR) and 95% confidence interval (CI) were obtained. A receiver operating characteristic curve (ROC) analysis and an area under the ROC curve (AUC) analysis were applied. Sensitivity and a function of 1-specificity were used to measure the accuracy of BDNF or/and TSPO for discriminating AIS patients from the normal controls. ANOVA and least square difference (LSD) post hoc test was performed to test the differences in biomarker levels in 5 sampling time (in hours) categories: <24, 24–48, 48–72, 72–96, 96–120. All of the calculations were performed using IBM SPSS software for Windows, version 23. All of the reported p-values were two-sided, and the significance was accepted at p < 0.05.

All statistical analyses were conducted using SPSS software for Windows (version 23.0). For the comparison of demographic and clinical data, the independent samples t-test was used for quantitative variables; the Chi-square test was used for categorical variables. Generalized linear models were used to assess associations between SED, biomarkers levels and psychopathology. We used linear, Poisson (for count data, e.g. CBCL scales) and gamma (for positively skewed distribution, e.g. serum TBARS and IL6 levels) distributions, as appropriate. Interactions between SED and biomarkers were assessed by adding the product term (i.e. SED*IL6) to the tested models. Due to the non-linearity of the models, the estimated β coefficients were transformed into rate ratio (RR) estimates. Post hoc correction to control for the false discovery rate was applied according to the Benjamini Hochberg procedure [52 ].

Data were expressed as mean ± SD. Comparisons were performed by two-sided independent Student’s test, one-way ANOVA analysis and χ² test using SPSS software for Windows version 23.0 (SPSS, Chicago, IL, USA). Kaplan-Meier survival curves were plotted and log rank test was done. Statistical significance was accepted when P < 0.05. All experiments carried out in this study were repeated three independent times.