Spss software version 20

Analysis was performed using SPSS software version 20.0.0. For the purpose of comparison patients were divided into DF and DHF (including grade I to grade IV). Categorical variables were recorded as frequencies and percentages while continuous variables were recorded as means and standard deviations (SD) unless otherwise stated. Categorical and continuous variables were analyzed using Chi-Square or Fischer-Exact test and independent t-test respectively. A logistic regression model was performed to determine the factors independently associated with severe form of dengue infection (DHF). The variables with P values less than 0.25 in univariate were considered as candidates for multivariate analysis. The use of univariate P values <0.25 has advantage of tending to include more variables in multivariate analysis while traditional levels of P value such as 0.05 can fail in identifying variables known to be important [24 (link)]. Receiver operating characteristics (ROC) curve analysis was used to determine the area under the curve (AUC) for prediction accuracy. Descriptive values below 5 % (p < 0.05) were considered statistically significant.

The current investigation utilized SPSS software version 20.00 (SPSS Inc., Chicago, IL, USA) for the evaluation of the impact of four different yogurt formulations and storage conditions on a range of parameters, encompassing pH, acidity, dry matter content, protein content, fat content, viscosity, probiotic viability, color measurements, total phenolic content, and total antioxidant capacity. A significance threshold of p < .05 was employed for detecting variations. To discern noteworthy differences among the results, one‐way ANOVA and Duncan's Multiple Range Test were applied. The experiments were performed in duplicate, with each analysis executed using three replicates.

The effect of NaOH concentrations on the properties of the CMSr film were investigated using SPSS software version 20.0 0 (SPSS Inc., Chicago, IL, USA). All measurements were analyzed in triplicate. The data were presented as the mean ± SD. Analysis of variance (ANOVA) was used to show a significant difference (p ≤ 0.05) by Duncan’s multiple range test (DMRT).

Descriptive data analyses were performed, along with bivariate and multivariate logistic regression for sex, age, smoking status, alcohol drinking status, physical activity level, khat chewing Status, fruit and vegetable consumption, BMI, WHtR, raised blood pressure, lipid profile (normal vs abnormal) as a confounding factor. All factors with a p value < 0.2 in the bivariate analysis were further analyzed with multivariate logistic regression analysis. The data were analyzed using SPSS software version 20.00 (SPSS Inc. Chicago, IL, USA), and p values < 0.05 were considered statistically significant.

We performed a univariate analysis using the χ² test (categorical variables) and the Kruskal–Wallis test (continuous variables). Then, immune biomarker variables with P<0.25 and interaction terms with P <0.05 in the univariate analyses were evaluated further for inclusion in the final ordinal or multivariable disordered multi-class logistic regression to obtain OR (frailty status or frailty subtypes as dependent variable) and aOR (age, sex, education, smoking) and their 95% CI. When necessary, quartiles of inflammatory markers were used for the χ² test and logistic regressions [47 (link)]. The highest quartile was used as the reference category. For the logistic regression, P≤0.05 was considered to indicate a statistically significant difference. The strength of the multicollinearity was examined using the variance inflation factor (VIF) and VIF>10 indicated that the model exhibited multicollinearity. The analysis was carried out using SPSS software version 20.0.0 (SPSS Inc., Chicago, IL, USA). Figures were prepared using STATA version 12.0 (STATA Corp., College Station, TX, USA) and GraphPad Prism version 8.0 (GraphPad Software, San Diego, CA, USA).

The data were analysed using SPSS software version 20.00 (SPSS Inc. Chicago, IL, USA). Data are expressed as means ± SEM. Statistical significance was assessed by one-way ANOVA followed by Tukey’s test. P < 0.05 was considered statistically significant.