The myocardial tissues in the infarct area or the equivalent area collected from 3 rats of the AMI group and the sham group were stored in liquid nitrogen prior to total RNA extraction using TRIzol reagent (TaKaRa, Japan). The RNA concentration was measured with a NanoDrop 2000 spectrophotometer (Thermo FisherScientific Inc., USA). Briefly, 1 µg of total RNA per sample was used for reverse transcription (RT) using the Reverse Transcription Kit (TaKaRa, Japan) according to the manufacturer’s protocol. The SYBR Green I assay (TaKaRa, Japan) was used for quantitative PCR based on the manufacturer’s protocol. Briefly, 2.0 µl cDNA, 0.4 µl RT primer, 10.0 µl 2 × Master Mix, and 7.2 µl RNase free H2O were used in a 20 µl PCR reaction system on an Mx3005P (Agilent Technologies, USA). The results were analyzed using the 2−ΔΔCT method. The data were analyzed using IBM SPSS statistic 19 software.
Spss statistic 19
Manufactured by IBM
Sourced in United States, Japan
SPSS Statistics 19 is a comprehensive and powerful software package for statistical analysis. It provides a wide range of data management, analysis, and presentation tools to help users gain meaningful insights from their data. The software's core function is to enable users to perform advanced statistical analyses, including descriptive statistics, regression, correlation, and more, to support informed decision-making.
Automatically generated - may contain errors
Lab products found in correlation
Prism 8, by GraphPad (2 mentions)
Reverse transcription kit, by Takara Bio (1 mentions)
Sybr green 1 assay, by Takara Bio (1 mentions)
Trizol reagent, by Takara Bio (1 mentions)
Mx3005p, by Agilent Technologies (1 mentions)
Origin 2021, by OriginLab (1 mentions)
Origin 2019, by OriginLab (1 mentions)
Microsoft excel 2016, by GraphPad (1 mentions)
56 protocols using spss statistic 19
1
Quantitative PCR Analysis of AMI in Rats
2
Validity and Reliability Assessment Methodology
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Of the 1272 users, 715 respondents (56.2%) who returned our questionnaires were enrolled
in this study for analysis. In addition to the construct validity test performed to
examine the validity, we used an internal consistency test, the split-half method, and the
test–retest method to assess the reliability.
We used SPSS Statistic 19 Software (IBM, Tokyo, Japan) for statistical analysis. Data
from the first survey were used for assessment of construct validity (major factor method
for the factor analysis and varimax rotation) and internal consistency (Cronbach’s
α coefficient), and for the split-half method (Spearman’s rank
correlation coefficient). In addition, data of 566 questionnaires from the first survey
with complete data were used for this analysis (valid response rate was 79.2%).
Furthermore, data from the first and second surveys were used for the test–retest method
to determine the Spearman’s rate correlation coefficient. Complete data sets of 352
respondents from both surveys (valid response rate was 62.2%) were included in this
analysis.
in this study for analysis. In addition to the construct validity test performed to
examine the validity, we used an internal consistency test, the split-half method, and the
test–retest method to assess the reliability.
We used SPSS Statistic 19 Software (IBM, Tokyo, Japan) for statistical analysis. Data
from the first survey were used for assessment of construct validity (major factor method
for the factor analysis and varimax rotation) and internal consistency (Cronbach’s
α coefficient), and for the split-half method (Spearman’s rank
correlation coefficient). In addition, data of 566 questionnaires from the first survey
with complete data were used for this analysis (valid response rate was 79.2%).
Furthermore, data from the first and second surveys were used for the test–retest method
to determine the Spearman’s rate correlation coefficient. Complete data sets of 352
respondents from both surveys (valid response rate was 62.2%) were included in this
analysis.
3
Relative Gene Expression Analysis
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The gene relative expression levels were calculated in Excel 2007. First, the CT values of 18S and 28S for all samples in the qRT-PCR reaction were calculated using geometric mean, and then the standardized values of these two reference genes were obtained [37 (link),40 (link)]. Finally, 2−ΔΔCT method was used to calculate the relative expression levels of target genes in each sample [41 (link)]. The statistically analysis of all data in this study was performed using IBM SPSS Statistic 19. Levene's test and independent sample t-test were respectively used for testing homogeneity of variance and significance among different treatments (α = 0.05). Log-rank (Mantel-Cox) test was used to analyze the significance of differences between survival curves from AP and MF treatments. Additionally, the Kolmogorov- Smirnova test was used to assess the normal distribution of fresh weight and follicle length. Logarithmic transformation was applied to data that did not conform to normal distribution before conducting statistical analysis. All figures were drawn in GraphPad Prism 8.0.1, and the data in the figures were represented as mean ± SD.
4
Comparative Analysis of Subject Characteristics
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The mean and standard deviation of subject characteristics were calculated for each group. Changes in subject values were analyzed using Student's t-tests comparing means between the test group and placebo-1 group or between the test group and placebo-2 group. Statistical analyses were performed using SPSS Statistic 19 (IBM, Armonk, NY, USA). P-values <0.05 were considered significant.
5
One-way ANOVA for 3xTg-AD Mice
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A one-way ANOVA was used for data comparison between 3xTg-AD and Non-Tg mice. ANOVA was conducted with IBM SPSS statistic 19 software where data were considered as statistically significant at p < 0.01.
6
Statistical analysis of experimental data
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Linear regression’s test was performed using software R version 3.6.3. Student’s t and Wilcoxon tests were performed using the SSC-Stat software (version 2.18; University of Reading, Reading, UK) and the IBM SPSS Statistic 19 software. The statistical significance of differences between groups was expressed by asterisks (*=p <0.05; **=p <0.01; ***=p <0.001).
7
Statistical Analysis of Experimental Data
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Data were analyzed using SPSS (IBM SPSS Statistic 19). Basic data were described by mean, min, max, SD and delta for numeric data. Deltas of test groups were assessed with a One-Way ANOVA test. P values less than 0.05 considered as significant.
8
Pancreatic Cancer Biomarker Analysis
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Data were analyzed using IBM SPSS statistic 19 (IBM, Armonk, NY, USA). The chi-square tests were used to determine the ENO1 expression differences between PDAC and adjacent noncancer tissues. The Wilcoxon rank-sum test and bilateral test were used in pairwise comparison of ENO1 and CA199 plasma levels between the groups. The correlation of plasma ENO1 level and the patient characteristics in the PDAC group was evaluated by Spearman's correlation analysis. To explore the correlation between ENO1 expression and prognosis of PDAC, the median of plasma ENO1 concentration in peripheral blood was used as the cutoff, and survival data were analyzed using the Kaplan-Meier method with a log-rank test for comparison. A logistic regression analysis was utilized to draw a receiver operating characteristic (ROC) curve. The area under the curve (AUC) was calculated to compare the performance of different biomarkers as a diagnostic test. P < 0.05 was considered to indicate a statistically significant difference.
9
Triplicate Experiments with Statistical Analysis
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All experiments were performed in triplicate and all data were presented as mean ± standard deviation (SD). Data were subjected to one-way ANOVA and Duncan’s significant difference analysis using SPSS software (IBM SPSS statistic 19). The significant level (p) was set as 0.05.
10
Smoking Prevalence and Associated Factors
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We performed a descriptive study of the prevalence of smoking among participants, and the frequency distribution of other variables. We performed bivariate analysis of factors potentially associated with smoking (yes/no) by comparing proportions between groups using the χ2 test. Results with p < 0.05 were considered statistically significant. For the multivariable analysis, we fit logistic regression models using the backward stepwise selection method to include variables that were relevant to the study, as well as those with p < 0.001 in the bivariate analysis, and to compute Odds Ratios (OR) and 95 % Confidence Intervals (CI). We used the Hosmer-Lemeshow test to evaluate the goodness of fit of each model. All analysis was performed using IBM SPSS Statistic19.
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