Statistical analysis Clausole campione

Statistical analysis. Statistical analyses will be performed using GraphPad Prism software (GraphPad Software). Data will be expressed as mean and SEM. A two-sided Student's t-test will be used to compare paired groups. Differences among groups were evaluated using two-way ANOVA. Correlation between two categorical variables will be analysed with chi-squeare test. Survival times were estimated using the Xxxxxx-Xxxxx method and compared using the log-rank test. Data will be considered significant when p¿0.05.

Statistical analysis. Categorical variables will be presented as frequencies and percentages, while continuous variables will be described using the median and interquartile range (IQR). Baseline categorical variables, such as histological analysis, will be assessed using appropriate statistical tests, including the chi-squared test or Xxxxxx'x exact test. Pairwise comparisons among groups will be adjusted using the Bonferroni correction method to account for multiple comparisons. Baseline continuous variables will be compared using the Xxxxxxx-Wallis test for multiple group comparisons, and Dwass, Steel, Xxxxxxxxx-Fligner statistics will be employed for post hoc multiple comparisons. Statistical significance will be determined at a threshold of p <

Statistical analysis. Descriptive statistics will be performed on demographic and clinical characteristics in the dataset. Results will be presented as mean and standard deviation for continuous variables or percentages (%) for categorical variables. Chi-square tests with Xxxxx¿ correction for continuity and Xxxx-Xxxxxxx tests will be used to compare categorical and continuous variables between groups. All clinical, demographic and serologycal data will ne integrated in a multidimensional analysis. Risk models will be developed with the use of decision-tree induction from class-labeled training records. Data will be analyzed by the statistical package Graph Pad Prism version 7.00 for Windows (GraphPad Software, La Jolla, CA). All analyses will be conducted at an ¿-level 0.05 significance.

Statistical analysis. For LNP formulations, three small-sized batches will be prepared and characterized in terms of colloidal properties and miR encapsulation efficiencies; the results will be expressed as average ±SD across the different batches. In vitro experiments will be replicated at least three times and the data will be expressed as average ±SD or ±SE of the mean (SEM). Statistical analyses will be performed using GraphPad Prism v8.0 software. In vitro and in vivo groups will be compared by Student's t test or Xxxxxxxx Signed Rank Sum Test as indicated, and statistical significance will be represented as follows: *p < 0.05; **p < 0.01; and ***p < 0.001. For identifying differentially expressed genes (DEGs) from RNA-seq data, a filter cut-off criterion of | log2FC|> 0.15 will be applied and genes with an adjusted p-value<0.05 will be considered as statistically significant. The biological function of DEGs will be identified by a Gene Ontology (GO) analysis using the R package ''enrichR'' (Xxxxxxxx et al, 2016). The criteria for the selection of the miRNAs/genes deregulated in MAPKi-resistant cells will be a significant t-test (P<0.05) and an at least two-fold change from baseline data. For in vivo studies statistical analysis will be performed using GraphPad Prism v8.0 software by applying Two-sided T-Test and conducting a correction for multiple comparisons. Biostatistics and Bioinformatics Unit of our Institute will give support to the analyses of the results obtained throughout the project.

Statistical analysis. To verify the normality of the distribution of the motor parameters a Kolmogorov-Smirnov test will be performed, while the Xxxxxx test will allow to check the homogeneity of the variance. The presence of outliers will also be evaluated, any anomalous values will be excluded from the analysis. Values that fall outside the following limits will be considered as anomalous values: Upper limit = upper quartile + 1.5 * (interquartile range) Lower limit = lower quartile - 1.5 * (interquartile range) To verify the discriminating capacity of the motor parameters, an Analysis of Variance analysis (ANOVA) will be performed to compare the group of healthy subjects with those with Parkinson's disease. In case of failure of the normality test, the analysis will be performed through the Xxxx-Xxxxxxx U test. To take into account multiple comparisons, the p-value of statistical significance will be corrected following the Benjamini and Xxxxxxxx procedure. For the motor parameters resulting significantly discriminating the reference limits of 95% and 90% will be calculated, corresponding respectively to the 2.5th and 97.5th percentile and to the 5th and 95th percentile. The reference limits will be calculated using both the parametric method (mean ± 1.96 * SD for reference limits at 95% and mean ± 1.64 * SD for 90% reference limits) and no parametric (2.5 and 97.5 percentile for reference limits to 95% and 5 to 95 percentiles for the 90% reference limits). For the calculation of the reference limits, the bootstrap method with 1000 bootstrap samples will be used. To evaluate the predictive value of the reference limits, the specificity calculated on the validation sample consisting of healthy subjects will be used, the sensitivity calculated on subjects with Parkinson's disease and the area under the ROC curve (AUC) calculated using the validation sample of healthy subjects and the sample of subjects with Parkinson's disease. The 95% confidence interval of both specificity, sensitivity and AUC will also be reported. The specificity observed in the validation sample will also be compared with the theoretical one of 97.5% or 95% depending on the significance limits used for the determination of the reference limits (95% or 90%). The comparison will be made using the test z for the comparison of a sample proportion with a theoretical proportion. The ICC calculated using ANOVA with 2 mixed-effect classification criteria will be used to evaluate the reproducibility of motor param...

Statistical analysis. We will perform analyses of variance (ANOVAs) on clinical infants' outcomes obtained at each timepoint with group (4 levels: siblings of children with ASD, preterm, SGA, and LR infants) as the main between-factor variable. Differences among groups (ASD siblings/preterm/SGA infants with or without a diagnosis of ASD/NDDs) will be analyzed using t-test or Xxxx-Xxxxxxx (Xxxxxxxx-Wallis) test - depending on data distribution - for continuous variables and chi square or Xxxxxx'x exact probability test for categorical variables. Additionally, models to track early developmental trajectories will be estimated in Mplus by latent class growth analysis with inter-individual variations in time of assessment and mixed-effects linear models with repeated measures to assess group differences in rates of longitudinal clinical changes. Cluster analysis will be used to detect infant subgroups with similar developmental patterns. Longitudinal trajectories of primary outcome variables will be modeled using generalized linear mixed models (GLMMs) with main effects of ASD/NDD outcomes at 36 months and time points along with their 2-way interactions. All available observations from each participant will be used in modeling via the GLMM. The GLMM will account for correlations between repeated measures within individuals, allowing for fixed and time-varying covariates and automatically handling missing data, thereby producing unbiased estimates if observations are missing at random. A model will be implemented to predict ASD/NDD outcomes based on both clinical and experimental measures (AIM 3). Both classic statistical approaches (e.g., logistic regressions and generalized linear models) and machine learning algorithms (e.g., Support Vector Machine, Random Forest, and Extreme Gradient Boosting) will be applied. Machine learning models will be trained on a subset of the sample and then tested in the remaining subset. Predictive performance of the models will be mainly measured using the receiving operating characteristic (ROC) curve analysis. The predictive performance of the different models will be compared with the DeLong method for comparison of area under curve. Motor, vocal and social features, EEG/ERP parameters and molecular variables collected in the first year of infants' life will be associated with ASD/NDD symptoms and related traits at 18, 24 and 36 months. Putative prognostic indexes of ASD/NDDs and their predictive power will be identified by multivariate logistic r...

Statistical analysis sWGS analysis As a general approach, data will be analyzed and summarized using mean and standard deviations, median and range for quantitative items and absolute counts and percentages for categorical items. Statistical analysis on sWGS data will be performed as described by Xxxxx et al. (3). The CPA score diagnostic performances will be analyzed using the ROC analysis and the Area Under Curve will be calculated together with its 95% confidence interval to evaluate how well the score can differentiate between true positive and true negative cases. Different cut-offs will be tested including that based on the Xxxxxx Index. Sensitivity, specificity, accuracy, Negative and Positive Predicted Values will be calculated at different cut-offs and the best threshold will be assessed. The stability of the identified threshold will be internally validated by a k- fold cross validation process.

Statistical analysis. Since the rCVD are rare and the models of ALIGNED project on procedure standardization, networking and IT platform implementation have no comparative references, only descriptive statistics will be applied. Descriptive statistics will be provided in term of absolute numbers and percentages for categorical data, means with standard deviations (SDs) and medians with value ranges for continuous data. Data analyses were performed using Student -test for continuous variables, chi-square tests for categorical variables, and Xxxxxxx exact tests. A value of <0.05 was considered statistically significant. Bivariate analyses will be performed to evaluate the number of access, the medium time for a definite diagnosis, the number of patients achieving a diagnosis or needing a diagnostic revision and/or implementation of further investigation and/or treatment as well as the number of patients lost al follow up. This analysis will be performed comparing a sample period of 14 months of the study in comparison to an equal period of 2018 and 2019 (pre-COVID-19). Logistic regression models with stepwise selection were used to determine the patient factors associated with disability and/or mortality during the project time periods. The statistical analysis of biological data will be oriented to evidence the differences in gene/protein/lipid expression between healthy and pathological phenotypes. For transcriptomics, the relative level of mRNA will be calculated by the 2- DDCt comparative method using the different specific housekeeping genes included in TaqMan Arrays. The GE analysis will be performed with BIORAD CFX Manager software (BIORAD, USA). All statistical analyses of deriving data will be performed using GraphPad Prism 5 and 8 (GraphPad Software, USA). The unpaired Student t test, one-way and two-way analysis of variance (ANOVA) followed by Bonferroni correction will be employed for evaluating significance in difference of means between groups, and values of p < 0.05 will be considered significant. For proteomics, data will be processed with Progenesis Qi for protein identification and statistical analysis. For lipidomics, the different classes will be compared by univariate tests. Then, for biomarker discovery, data will be uploaded and processed for multifactorial analysis with MetaboAnalyst. Partial least squares discriminant analysis (PLS-DA) will be performed in order to increase the group separation, and investigate the variables with a VIP score >1, which wil...

Statistical analysis. Each experiment was repeated at least three times, as biological replicates; means and standard deviations between different experiments were calculated. Statistical p-values obtained by Student t-test were indicated: three asterisks *** for p-values less than 0.001, two asterisks ** for p-values less than 0.01 and one asterisk * for p-values less than 0.05.

Statistical analysis. To examine the ideal set of parameters collected from the multimodal dataset to forecast the patient's clinical condition (GOOD or POOR result), OU3 will create artificial intelligence (AI) models. The suggested AI models will be trained using a variety of TKA patient multivariate data. Data preprocessing, data segregation, modeling, model prediction, and performance measurements are the five primary sequential levels of the architecture pipeline that will define the AI strategy. When operating, pipelines take a linear approach to data transformation. Data preparation, or the first stage, entails the pre-processing of multimodal data. Data gathering, data visualization, feature selection, and data transformation are all phases involved in this process. The methodology takes care of missing data, eliminates existing outliers, normalizes to a predetermined range, and chooses characteristics based on their influencing potential. The feature selection stage uses the data visualization's result as its input. When used, this technique gives each feature a "feature score." The feature with the highest score is most relevant to the dependent variable. The relationships between multimodal data will then be found, and the fusion features will be built. Although there have been a few studies that have attempted to combine imaging data with clinical and kinematic data in the orthopedic domain, we will test an approach that has been successfully applied in a different field of science [4-7], creating an appealing fusion scheme for multimodal fusion research. To find connections between particular imaging measurements and clinical data, we'll employ a more useful correlation analysis technique. Candidates for the construction of 'imaging-clinical pairs' include Xxxxxxx correlation analysis, canonical correlation analysis, and distance correlation analysis. We will feed pertinent feature sets into the AI model for the training stage after doing univariate and multivariate analysis. This level is a part of the data separation, which entails dividing the dataset into train, test, and validation sets of data. This stage main goal is to prevent overfitting and improve prediction of the underlying real-clinical result. Model training, model assessment, cross-validation, and hyperparameter tweaking via model validation are the four sub-levels that make up the third stage. This includes the actual working of AI, where various AI classifiers such as Random Forest, Naive Xxxxx, a...