Rstudio version

Each respondent was assigned to a consumer group based on their individual PSS-10 score. Thus, n = 116 (61%) belonged to the ‘Moderate stress’ group, and n = 74 (39%) to the ‘High stress’ group. All consecutive statistical analyses were based on these two groups. Mean (±SD) or median values (IQR) were calculated for each variable for each consumer group, and results were illustrated by either bar plots based on the mean values, or by stacked bar plots showing the distributions of answers by the two groups. The normal distribution of the data was checked by the Shapiro–Wilk test, and subsequent statistical tests were chosen accordingly. Wilcoxon’s signed rank test was utilized for detecting significant differences between the two groups on numerical vales, whereas Chi² tests were used for the categorical variables. For comparing changes in meal patterns before being stressed and now within each group, McNemar’s test was used. All data analyses were conducted in R Studio©, version 1.3.1093 (Boston, MA, USA) [50 ]. Statistical significance was set to α < 0.05 for all calculations. For 0.05 < α < 0.08, results were reported as ‘trending’ towards a significant difference.

A comprehensive and systematic literature search in PubMed was performed for infliximab population pharmacokinetic analyses in patients with IBD. The search terms were “infliximab” AND “population” AND “pharmacokinetics” and reference lists of identified articles were manually screened for further eligible studies. Subsequently, modeling and study information was collected, including model structure, population pharmacokinetic parameter values, covariates, inter-individual variability, residual variability, information on patient cohorts, disease type, number of patients, number of collected blood samples, and ADA immunogenicity rate. The population pharmacokinetic models described in the gathered studies were implemented and evaluated using the nonlinear mixed effects modeling software NONMEM^® version 7.4 (Icon Development Solutions, Ellicott City, MD, USA). Computations for prediction- and variability-corrected visual predictive checks (pvcVPCs) were generated with the PsN (version 4.9.0) tool “vpc” [39 (link),40 (link)]. Data management, graphics, and quantitative model diagnostics were carried out using the R programming language version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) and R Studio^® version 1.2.5019 (R Studio, Inc., Boston, MA, USA).

Epitope predictions for significant HLA-to-viral codon associations were done using TepiTool (39 (link)). Epitopes 15 AAs in length covering the codon of interest were provided to the program, and the associated HLA allele was selected for a high number of peptides using TheImmune Epitope Database (IEDB) recommended prediction method. The recommended significance threshold for selecting potential binders is % rank of <1 and IC50 <500 nM (40 (link)). Analysis 2 (Fig. 1) investigated the association of host allele dosage-to-VL and variant viral AA-to-VL using a two-sample t-test. t-Tests and box plots were done in RStudio version 1.3.1056 (41 ). The significance threshold was set at P < 0.05.

Experiment data were analyzed by Student’s two-tailed t-test and one-way ANOVA using the RStudio version 3.6.0 with a model that included fixed effect of DGAT1 and controls (WT, VC, and null-sibling). A multiple comparison of treatments such as Bartlett’s test (homogeneity of variances) and Shapiro-Wilk normality test from ANOVA was used to highlight significant among treatment means while P-values were adjusted by the BH method (Benjamini and Hochberg, 1995 (link)) to control the false discovering rate. Means and SE are reported, and fixed effects declared significant from P < 0.05.

Data was extracted independently by primary author (RH) and checked by a second author (ND) using the JBI Data Extraction Form for Review for Systematic Reviews and Research Syntheses [23 (link)]. This included recording information on author, year of publication, country of origin, objectives, results, appraisal, appraisal instruments, appraisal rating, and other relevant information on the primary level studies included in the review.
In addition to completing the JBI extraction checklist for each included review, the standardized mean difference (SMD), 95% confidence intervals (CI), and number of studies included for all eligible meta-analyses were extracted. If a pooled effect was not available for a given study, a random effects model was run to calculate the missing values using the available mean, standard deviation, and number of participants for the intervention and control groups. This model was conducted using the metafor function in R (R Studio, Version 1.2.1335).

Modeling was performed using the language R (RStudio Version 1.0.143–© 2009-2016 RStudio, Inc.). Approx. 70% of cases from each group were classified into the train set (133 cases); AM group (66 cases) and SFT/HPC group (67 cases) were used to establish the model. The remaining 30% were classified into the test set (59 cases), AM group (29 cases) and SFT/HPC group (30 cases), to verify the accuracy of the established model.
A comparison of texture features in T1WI sequences was analyzed using independent sample t-test and Kruskal-Wallis test; a P value < 0.05 was considered statistically significant. Univariate logistic regression analysis (P < 0.05) and Spearman's correlation analysis (P ≥ 0.05 or P < 0.05, r < 0.9) were used to screen for the parameters with high predictive power. T2WI and contrasted T1WI sequence texture feature used the Lasso method to reduce dimensionality and selected high-performance parameters. Parameters with high predictive power in the three sequences were further eliminated using the stepwise iterative method, and the remaining high-performance parameters were fed into a multivariate logistic regression analysis to determine an optimal logistic regression model for tumor classification. The confusion matrix was used to analyze the accuracy of the model. ROC curve was constructed to assess the grading ability of the logistic regression model.