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Sas software 7

Manufactured by SAS Institute
Sourced in United States

SAS software 7.1 is a comprehensive data analysis and statistical software suite. It provides a variety of tools for data management, analysis, and reporting. The software is designed to handle large and complex datasets, enabling users to extract insights and make informed decisions.

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Lab products found in correlation

7 protocols using sas software 7

1

Comparative Analysis of Cell Viability

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Within each experiment, results were described using mean ± standard deviation (SD). Significant differences between treatment and control as well as the malignant and benign cells were calculated by Dunnett’s Multiple Comparison Test or Student’s t-test. Differences were considered statistically significant for p < 0.05. All tests were performed with SAS software 7.1 (SAS Institute Inc., Cary, NC, USA).
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2

Quantitative Real-Time RT-PCR and Cell Killing Analysis

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The results are given as the mean of at least three independent experiments for quantitative real-time RT-PCR and cell killing using GNOME-LP. Statistical analysis was performed using SAS software 7.1 (SAS Institute Inc., Cary, NC, USA). Significant differences in gene expression of CLDN-3, -4, and -7 were calculated using Student’s two-sided t-test. Statistical analysis of cell killing using GNOME-LP was performed using Dunnett´s multiple comparison test and Student´s two-sided t-test. Differences were considered statistically significant for p < 0.05.
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3

Cell Proliferation Analysis using SAS

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Statistical analysis was performed using SAS® software 7.1 (SAS Institute Inc., Cary, NC, USA). Cell number, viability, and proliferation index were evaluated using the global F-test from the one-way analysis of variance followed by pairwise multiple means comparisons with the least significant difference test. p-values < 0.05 were considered to be statistically significant.
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4

Comprehensive Statistical Analysis of Cellular Response

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All statistical analyses were performed using SAS software 7.1 (SAS Institute Inc., Cary, NC, USA); the significance threshold was set at p ≤ 0.05. Data distribution was tested using Shapiro-Wilk test. Statistical analysis of BrdU cell proliferation test, growth curves, and real-time PCR expression analyses of HMGA2 and CD44 were performed using a 2-tailed Mann-Whitney-U test. Times to in vitro wound-scratch closure were analysed by two-tailed t-test. Results from MTS cell proliferation test and flow cytometry were evaluated using Wilcoxon’s two-sample test.
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5

Metabolic Activity and Apoptosis Assay

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Statistical analysis of data was performed with SAS software 7.1 (SAS Institute Inc., Cary, NC, USA). Means and standard deviations are displayed. For comparison to untreated controls in metabolic activity, cell count and proportions of vital, apoptotic and late apoptotic cells, Dunnett’s test for multiple comparisons was applied. Additionally, Student’s t-tests were performed for comparison between PGE2 concentrations at 1 and 10 μM meloxicam. The confidence levels were set to 95% (p < 0.05; *), 99% (p < 0.01; **) and 99.9% (p < 0.001; ***). All experiments were conducted three times independently, except from cell count and apoptosis rate of doxorubicin and carboplatin response, which were conducted four times and from PGE2 concentrations, which were measured once.
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6

Comparative Analysis of Data Using SAS

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Statistical analysis of data was performed with SAS software 7.1 (SAS Institute Inc., Cary, NC, USA). For comparison of two means, two-tailed t-test was used. The confidence value was set to 5% (p<0.05) and was considered statistically significant.
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7

Quantitative RT-PCR and GNOM-LP Cell Killing

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The results are given as mean of at least three independent experiments for quantitative real-time RT-PCR and cell killing using GNOM-LP. Statistical analysis was performed using SAS software 7.1 (SAS Institute Inc., Cary, NC, USA). Signi cant differences in gene expression of CLDN3, -4 and - 7 were calculated using Student's two-sided t-test. Statistical analysis of cell killing using GNOME-LP was performed using Dunnett´s Multiple Comparison Test and Student´s two-sided t-test. Differences were considered statistically signi cant for p < 0.05.
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