Spss statistics software 25

Ocean data view software was used to visualize maps of sampling stations and the concentration of A. coerulea eDNA measured by qPCR. To compare the concentration of A. coerulea eDNA between sediment and seawater samples, the gene copy numbers for seawater samples were converted to the same unit as for sediment samples, that is, copies/g, based on the seawater density obtained in the previous step (Table S1). Origin 95 software was used to show the line chart of the concentration of A. coerulea eDNA in the laboratory degradation experiment.
Kruskal–Wallis nonparametric tests in SPSS Statistics software 25 (IBM Corporation) were used to test the differences in seawater environmental factors and the differences in the concentration of A. coerulea eDNA between Days 0, 5 and 10 in the laboratory experiment and among various stations in the field. The Mann–Whitney nonparametric test was used to analyze the differences in the concentration of A. coerulea eDNA between two depths (surface and bottom layer seawater) and two environments (seawater and sediment). A Spearman rank correlation analysis was used to identify the correlation between the concentration of A. coerulea eDNA and five environmental indicators (temperature, DO, salinity, pH and Chl) in surface and bottom seawater samples in July and August, respectively.

Given that most variables did not show a normal distribution, nonparametric tests were used for statistical comparison. Continuous variables are displayed as medians (minimum–maximum), and categorical variables are displayed as counts (percentages). The Mann–Whitney U test was used to compare two independent groups. Group comparisons for categorical variables were performed using the chi-square test. The level of significance was 0.05 (2-sided) for each statistical test. P-values concerning secondary endpoints were considered exploratory and are presented without Bonferroni correction. Factors with P-values less than 0.2 were enrolled in a Cox hazard regression model to identify independent risk factors. Kaplan–Meier estimates were calculated for the healing rate with the last available contact date (follow-up = time to event). The log rank test was used for the comparison between patients without and with CD. We assumed that loss to follow-up was missing not at random (MNAR) and did not address this with specific statistical measures. Statistical analysis was performed using SPSS Statistics Software 25.0 (IBM, Armonk, NY, USA).

Studies available at the time of protocol development demonstrated that imiquimod induced regression of cervical disease in 73%, and LLETZ in 95% of patients [16 (link)]. Using these regression rates, the desired 80% power of the study and an alpha of 5%, we calculated that, based on the primary objective and outcome measure, the sample size required 52 women in each arm. Calculations were performed using the G*Power application [30 (link)]. Statistical analysis was performed using SPSS Statistics software 25.0 (IBM, Armonk, NY, USA). Descriptive statistics were calculated on basic patient characteristics. Pearson’s chi-square/Fisher’s exact tests were used for comparison of categorical data between groups, and Student’s t-test for independent samples was used to compare normally distributed data between groups. Data was analysed using the intention-to-treat (ITT) principle, meaning that all enrolled patients were included in the final analysis, including those who did not completely adhere to the assigned treatment or did not complete it. The only exception from this principle was the evaluation of side effects in the experimental arm at 10 and 20 weeks after treatment initiation, where a per-protocol analysis was performed. A post hoc subgroup analysis was performed for patients with CIN2p16+ and CIN3 lesions. Statistical significance was set at a p-value < 0.05.

All statistical analyses were carried out using IBM SPSS Statistics software 25.0 (IBM, Armonk, NY, USA). Wilcoxon signed rank test was used for statistical analyses. Pearson's correlation test was used for correlations. p‐values <0.05 were considered as statistically significant.

Continuous variables were expressed as median with interquartile range (IQR) and were analyzed using non-parametric tests (Mann–Whitney U test). Categorical variables were expressed as the number of patients and percentages. For group comparisons concerning categorical variables, the chi-square test was used. The influence of perioperative variables on PPOI and its types was evaluated by univariate analysis. The variables that showed in the univariate analysis a relative association with PPOI or its types with a p value less than 0.15 were enrolled in a multivariate logistic regression model to adjust for potential confounders and to identify potential independent risk factors. A p value of less than 0.05 was considered statistically significant for the purposes of this study. No Bonferroni correction has been performed due to the exploratory character of this investigation. Odds ratios (ORs) were calculated with a 95% confidence interval (CI). The statistical analysis was performed with SPSS Statistics Software 25.0 (IBM, Armonk, NY, USA).

Categorical variables were presented as percentages (%). Quantitative variables were presented as median ± standard deviation (SD). To compare continuous variables the Mann-Whitney U test was used. Categorical variables were compared using the chi-square test. Fisher’s exact test was utilized for analyzing categorical data when the sample size was small. Univariable analysis was used for our primary endpoint anastomotic leakage and following secondary endpoints: rates of overall postoperative complications, reoperation, length of hospital stay, and CD recurrence. Only significant variables from the univariable analysis were entered into the multivariable regression model. A p-value of ≤ 0.05 was considered statistically significant. For secondary endpoints, p-values were considered exploratory and presented without Bonferroni correction. Statistical analysis was performed using SPSS Statistics Software 25.0 (IBM, Armonk, NY, USA).