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Mechanisms of Immune Escape in B Cell Lymphoma
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Mechanisms of Immune Escape in B Cell Lymphoma
Results
This section will present results that have been analysed statistically. Graphical
presentations generated by the BD Accuri C6 Plus flow cytometer, t-tests for comparing the
means of CD8 and CD14 biomarkers expression for each sample, ANOVA one-way test, graphs,
and linear regression will be presented. Alongside, the output of the data analysis will be
presented.
Quantitative data was collected from the flow cytometer after conducting the experiment,
and data analysis was conducted using inferential statistics, linear trend lines, and graphing. The
research hypothesis was that CD8 and CD14 would change their behaviour in the presence of
LILRB1, which is linked to B cell lymphoma diagnosis.
Flow cytometry is a laboratory technique enabling researchers to detect or measure the
physical or chemical characteristics of a population of cells or particles (Krishhan et al., 2009).
This technique was used in this research to monitor how human cells interact with the presence
of LILRB1; a gene was hypothesised to facilitate immune escape in B cell lymphoma. BD
Accuri C6 Plus flow cytometer was used as the instrumentation as described earlier. This
research hypothesises that LILRB1 improves immune escape in the cancerous cells in the
lymphatic system. After reviewing the literature, many genes or biomarkers were hypothesised to
mark the immune escape in B cell lymphoma, including the CD8 and CD14 biomarkers.
Hence, the flow cytometer was used to measure the CD8 and CD14 biomarker expression
after 0 hours, 4 hours, and 24 hours after incubation. Mean PR-A readings from the BD Accuri
C6 Plus flow cytometer software were collected to conduct data analysis. Statistical analyses
were conducted using Microsoft excel 2013. Overall, seven samples were prepared for this
experiment, labelled as (control 1, control 2, sample 1, sample 2, sample 3, sample 4, and sample
5). For control 1 and control 2, specimens included blood samples from healthy humans and all
the reagents mentioned in the protocol but had Mouse IgG1kappa in place of the anti-LILRB1
antibody to be used to set the background fluorescence levels on the cytometer. It is worth
mentioning that blood samples in all specimens were collected from healthy human individuals.
This experiment is an in-vitro experiment ensuring safety for all participants.
The researcher collected mean PE-A readings and M1 readings from the BD Accuri C6
Plus flow cytometer software. However, data analysis was conducted on mean PE-A values, as
they reflect a numerical and quantitative measurement of the CD 8 or CD 14 biomarker
expression after different incubation times and per the previously discussed protocol for the
experiment.
Graphical presentations generated by the BD Accuri C6 Plus flow cytometer
Figure 1 and Figure 2 show a group of graphical presentations for the PE-A outputs
measured by the BD Accuri C6 Plus flow cytometer. Figure 1 showed the graphical outputs for
samples 1, 2, 3, 4, and 5 when the expression of CD8 was quantitatively measured. After the first
glance, it is noted in figure 1 that the detected quantity of the CD8 biomarker is the highest after
incubation for 24 hours. In figure 2, the graphical outputs are presented for samples 1, 2, 3, 4,
and 5 when the expression of CD14 was quantitatively measured. Similarly to the previous
figure, the highest expression of the CD14 biomarker occurred after incubation for 24 hours.
Another note to mention from figure 1 and figure 2 is that expression of CD8 and CD14 was
present in all graphs, in small or large amounts. In figure 1, it is noted that sample 4 showed the
highest CD8 expression at 0 hours incubation, which did not happen in other samples.

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Figure 1: the graphical presentation of PE-A readings for CD8 biomarker for samples 1, 2, 3, 4, and 5
Figure 2: the graphical presentation of PE-A readings for CD14 biomarker for samples 1, 2, 3, 4, and 5

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The researcher collected the mean of the numerous PE-A values which are generated by
the BD Accuri C6 Plus flow cytometer software. Statistical analysis and graphing using
Microsoft Excel 2013 was conducted to build robust conclusion regarding the present raw data.
Notably, specimens of control 1 and control 2 were subjected to the BD Accuri C6 Plus flow
cytometer at 0-hour incubation only. Hence, there is no available data for control 1 and control 2
specimens at 4 hours and 24 hours incubation time.
T-tests and ANOVA one-way data analysis were conducted as inferential statistical tests.
P-values under 0.05 were agreed to show the statistical significance of the data. T-tests were
done five times, specifically for sample 1, sample 2, sample 3, sample 4, and sample 5. In those
t-tests, the data linked to CD8 and CD14 biomarkers expression were compared to compare the
means of the two groups. On the other hand, ANOVA one-way tests were done twice, one time
for CD8 biomarker expression-related data linked to all samples and one time for CD14
expression-related data for all samples. ANOVA one-way tests were done to sample 1, sample 2,
sample 3, sample 4, and sample 5 data. Control 1 and control 2 data were excluded from t-tests
and ANOVA one-way tests due to the fact that data at 4 hours and 24 hours incubation times
were not measured. Table 1 shows the mean of PE-A readings of biomarkers CD8 and CD14
expression generated by the BD Accuri C6 Plus flow cytometer.
CD 18 mean PE-A
Cd 14 mean PE-A
SAMPLE
O hr
4 hr
incubation
period
SAMPLE
O hr
incubation
period
4 hr
incubation
period
24 hr
incubation
period
Control 1
6078.72
NA
Control 1
1293.36
NA
NA
Control 2
741.29
NA
Control 2
934.88
NA
NA
Sample 1
625.43
559.97
Sample 1
1219.16
712.2
1274.38
Sample 2
401.47
645.99
Sample 2
741.15
789.87
1718.21
Sample 3
299.61
97.41
Sample 3
1041.75
232.59
3516.37
Sample 4
2462.93
483.67
Sample 4
137.8
564.13
4269.26
Sample 5
194.87
189.51
Sample 5
521.93
1362.71
3706.02
Table 1: the mean of PE-A readings of biomarkers CD8 and CD14 expression generated by the
BD Accuri C6 Plus flow cytometer for control 1, control 2, and sample 1, sample 2, sample 3,
sample 4, and sample 5.
Inferential statistics were chosen to find p-values. Notably, inferential statistics can help
in explaining phenomena. It was integral to conduct two different types of inferential statistics
tests to document a higher amount of meaningful results.
T-tests for comparing the means of CD8 and CD14 biomarkers expression for each sample.
Data analysis ToolPak by Microsoft excel 2013 was used to generate t-tests. The
conducted tests were t-tests: paired to sample for means. The purpose of this test is to compare
the means of CD8 and CD14 biomarkers expressions for each sample (Kim, 2015). For sample 1,
where the t-test results are presented in Table 2, the mean of each biomarker expression is
different, where the mean of CD14 biomarker expression is 1068.58 compared to 663.49 for
CD8. The Pearson correlation coefficient is 0.77, which justifies a moderately high correlation
between the two datasets. However, the p-value (two-tail) is 0.091 (higher than 0.05). The
number of observations for each variable is only 3, limiting the t-test's ability to find robust
differences between the two datasets. P-value, which is based on a two-tailed t-test, was chosen
due to its compatibility with numerous research purposes, including medical research.

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1 Mechanisms of Immune Escape in B Cell Lymphoma Student name University Course Instructor Date 2 Mechanisms of Immune Escape in B Cell Lymphoma Results This section will present results that have been analysed statistically. Graphical presentations generated by the BD Accuri C6 Plus flow cytometer, t-tests for comparing the means of CD8 and CD14 biomarkers expression for each sample, ANOVA one-way test, graphs, and linear regression will be presented. Alongside, the output of the data analysis will be presented. Quantitative data was collected from the flow cytometer after conducting the experiment, and data analysis was conducted using inferential statistics, linear trend lines, and graphing. The research hypothesis was that CD8 and CD14 would change their behaviour in the presence of LILRB1, which is linked to B cell lymphoma diagnosis. Flow cytometry is a laboratory technique enabling researchers to detect or measure the physical or chemical characteristics of a population of cells or particles (Krishhan et al., 2009). This technique was used in this research to monitor how human cells interact with the presence of LILRB1; a gene was hypothesised to facilitate immune escape in B cell lymphoma. BD Accuri C6 Plus flow cytometer was used as the instrumentation as described earlier. This research hypothesises that LILRB1 improves immune escape in the cancerous cells in the lymphatic system. After reviewing the literature, many genes or biomarkers were hypothesised to mark ...
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