Spss software ver 21

All statistical analyses were performed using SPSS software ver. 21 (SPSS Inc., Chicago, IL, USA). Data are expressed as the mean ± standard deviation (S.D.). One-way ANOVA followed by post hoc Student-Newman-Keuls test was used for analysis of data; different characters were used to show significance at P < 0.05.

Species richness and UniFrac distances were analyzed by the Wilcoxon rank sum test (two-tailed) using R version 3.0.2 [65 ]. Correlation test of skin pigmentation was performed by the Pearson correlation with the SPSS software, ver. 21 (Armonk, NY, US). Association of host genotype with skin humidity was tested using the Kruskal-Willis test.

A descriptive analysis was conducted on all patients, as well as based on the type of ventilator used. For quantitative variables, mean ± standard derivation (SD) or median with interquartile range (IQR: 25–75%) were used, and comparisons were made using appropriate statistical tests such as Student’s t test or the Mann–Whitney U test. Categorical variables were expressed as percentage (%) and compared using the chi-square test or the Fisher’s exact test when applicable. Survival curves were generated using the Kaplan–Meyer method, and comparisons were made using the log-rank test and Cox model. Univariate and multivariate Cox proportional hazard regression analysis were performed to identify prognostic factors, particularly related to the type of ventilator and one-year survival rate. Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported to compare hazards between patients' groups. To avoid over-fitting in the multivariate model, only factors with a p-value less than 0.2 in the univariate analysis were included. The final model was selected using forward conditional selection. All statistical analyses were performed using SPSS software (ver. 21) (SPSS Inc., Chicago, IL) and GraphPad Prism9© (GraphPad Software Inc., La Jolla, CA). A two-tailed p-value of < 0.05 was considered statistically significant in all analyses.

Data analysis was carried out using the SPSS software ver. 21 (SPSS, Chicago, IL, USA). The frequency percentage was used to describe the positive and negative samples by two methods and for frequency of recognized antigens in each group of patients. Sensitivity for western blot and IFAT was defined as the number of samples with a western blot or IFAT-positive result/the number of samples with a positive diagnosis by DAT and skin culture × 100.

Continuous variables are presented as means ± standard deviation (SD), while categorical variables are shown as numbers and relative frequencies. The baseline demographic and clinical characteristics of CA patients were compared based on CPR duration (≤ 40 min vs. >40 min) using the Student’s t-test or Mann-Whitney tests for continuous variables and the Chi-square or Fisher’s exact tests for categorical variables. Univariate and multivariate binary logistic regression analyses were performed to assess the prognostic value of CPR duration, presenting results as odds ratios (OR) and 95% confidence intervals (95% CI). Variables with a P-value < 0.2 in the univariate analysis were included in the multivariate analysis. Additionally, linear regression was used to evaluate the prognostic value of CPR duration based on laboratory tests. All analyses were conducted using SPSS software (ver.21) (SPSS Inc., Chicago, IL, USA), and a two-tailed P-value of < 0.05 was considered significant. GraphPad Prism 9© (GraphPad Software Inc., La Jolla, CA) was utilized to create a forest plot for the multivariate logistic regression analysis, identifying independent factors associated with the duration of CPR.

PFS was a primary endpoint in the evaluation of the prognostic significance of interim PET/CT. PFS was defined as the time from treatment to disease progression or death of any cause, while overall survival (OS) was defined as the time from treatment to death from any cause. The Kaplan–Meier method was used to estimate the PFS and OS, and the survival curves were compared using a log-rank test. To evaluate the optimal cutoff value of SUVmax for predicting PFS, receiver-operating characteristic curve (ROC) analysis was performed. The estimate of the relative risk of event and its 95% confidence interval (CI) for PFS and OS were assessed by univariate and multivariate analyses using a Cox proportional hazard model. All of the statistical computations were performed using SPSS software (ver. 21; SPSS Inc., Chicago, IL, USA). The P values < 0.05 were considered significant in all of the analyses.