NHRP!

James Bedford | Nuffield Health Research Placement | Biomedical Sciences September 2015

Cord Blood Stem Cell Storage THE EFFECT OF UMBILICAL CORD BLOOD SAMPLE SIZE AND COLLECTION PROCESS ON CD34 AND CD45

CELL CONCENTRATION

JAMES BEDFORD 2015 PAGE 1

Introduction

Umbilical cord blood (CB) is blood taken directly from the umbilical cord at birth. Rich in

hematopoietic stem cells (HSCs), this blood has significant potential to provide support

for gene therapy, tissue regrowth and oncological treatments. The stem cells taken from

the umbilical cord exhibit a high degree of pluripotency and can, therefore, be used as an

alternative to the intrusive extraction of bone marrow stem cells which is currently the

primary other source of such high potency stem cells. When bone marrow stem cells are

employed to combat diseases or restore bodily damage, patients face the potential of

rejection issues (GvHD). Stem cells taken from adult tissue also suffer from environmental

damage as a result of the donor’s age. Umbilical stem cells are taken from an otherwise

discarded tissue, suffer no environmental damage and can be used to guarantee a 100%

match for the baby from which it was taken, should they require stem cell treatments in

the future.

With over 10 years of experience, Biovault Technical Ltd. is a world leading human tissue

bank and the UK’s largest private tissue bank. Partnered with both the NHS and Plymouth

University, an accredited leader in best practice, Biovault stores embryonic umbilical cord

blood, cord tissue, peripheral blood stem cells, bone and bone marrow stem cells, femoral

heads and tendons in -190° cryogenic storage vats ready for a time such that they may be

required by any of their 11,000-strong client base. Biovault primarily receives samples of

umbilical cord blood and tissue from clients and partners throughout the world, processes

them in-house and performs blood stem cell testing and sample analysis before storing

them in cryogenic Eternes for up to 30 years.

Research Project

When Biovault processes incoming umbilical cord blood, they recover samples to be

analysed through flow cytometry. Flow cytometry uses fluorochromes bound to antigens

specific to the particular stem cells in the cord blood in order to record the number of

viable stem cells in any one sample and thus the feasibility of those stem cells in any

treatment using an array of lasers and high-sensitivity sensors. The number of leukocytes,

stem cells and their according viabilities are measured to determine the likely success of

samples after the freezing process. The viability of stem cell samples are not all equal and

if many cells are in a state of apoptosis before freezing, these cells may be recorded as

viable during flow cytometry, thus hyper-inflating the result. Viability often decreases over

the storage period of the samples and hence the incoming viability of the samples is

critical.

Biovault receives samples from over 10 different countries, from women aged 20 to 70

employing various methods of birthing and different birth weights subsequent to differing

gestation periods – all of these factors may influence the feasibility of any sample that

Biovault receives; and that is what this investigation seeks to establish.


My report’s title is ‘The effect of umbilical cord blood sample size and collection process

on CD34+ and CD45+ cell concentration’. I initially set out to investigate two broad

hypotheses that Biovault were particularly interested in. The first was that babies

delivered after 40 weeks gestation gave lower sample volumes with lower stem cell counts

and lower viability. The second was that older mothers gave samples of a decreased

viability, volume and stem cell count.

Scientific theory states that younger mothers should give healthier babies with a lower

risk of birth complications and that older women present a higher risk for birth defects

and poorer-quality births. Previous studies (Ghadeer Ibrahim Alrefaei, 2015) have

supported the idea that maternal age shares a negative correlation with MSC

(Mesenchymal Stem Cells) count in the placenta although the effect on HSCs is relatively

undocumented. Older studies (K K Ballen, 2000) with lower precision instruments have

found that for hematopoietic stem cells (the variety present in the umbilical cord blood),

maternal age had no effect on the count or viability of the sample. However, with a

noticeable lack of data in this area, this investigation (with a greater sample size and

equipment sophistication than that available to most prior reports) seeks to shed light on

these effects.

Recovery and Method

The processing at Biovault is based around the findings of Pablo Rubenstein (Rubenstein,

1995). Incoming samples arrive in blood bags merely a day or two after collection at birth.

Arriving by courier, the incoming samples are logged into the system, uniquely identified

by label and QR code and assigned to an individual paper and electronic file which will

stay with the sample for the duration of its lifetime. Once checked in, samples are

prepared for processing by scrubbing with Biocide E (a sodium hypochlorite-based

sporicide) to kill any spores present on the samples before cleaning with IPA (99.8%

Isopropyl Alcohol) which ruptures the cell walls on any remaining bacteria.

Samples are then passed into a grade B cleanroom through an air-sterilising hatch. After

replacing their clothes with two sets of sterile gowns, masks, hoods and gloves, staff enter

the cleanroom and receive the cleaned blood bag. The blood bag is hermetically sealed to

a three-part blood bag before the blood undergoes centrifugation at 1500rpm for 15

minutes.

Centrifugation separates the blood into its three constituent components by relative mass

(heavy erythrocytes, the buffy coat of concentrated leukocyte suspension and the light

plasma layer) before a laser-guided Optipress is used to compress the anticoagulated

blood into the three blood bags based on the type of blood component. In grade A safety

cabinets samples of blood plasma are taken for blood tests. Dimethyl sulfoxide (DMSO), a

powerful cryoprotectant is then added to the bag of stem cell-containing leukocytes to aid

in the process of vitrification during freezing. DMSO is toxic to cells at room temperature

and after the addition of DMSO, samples must be drastically cooled by to avoid rapid cell


death within ten minutes. DMSO addition takes two minutes and in the remaining eight

minutes, the leukocyte suspension is sampled, transferred to a final twenty-five millilitre

storage bag, hermetically sealed four times to AABB (American Association of Blood

Banks) regulation before the storage bag is packaged into a protective cardboard cassette,

security sealed, labelled and verified for storage.

Within the final 2 minutes, the processed cassettes and samples are moved to be rapidly

rate cooled to -140°C. Without the DMSO, cells in the sample would form ice crystals

during the cooling process which would lead to the rupture of plasma membranes and

subsequent cell death (Health, 2011). After being cooled for 75 minutes, the samples are

removed to a Cryocart (a liquid nitrogen-filled mobile transporter) before being placed

into a dedicated and logged position within an Eterne for long-term storage.

After placement in the Eterne, records of all equipment and lot numbers are recorded in a

secure database and samples of maternal blood and processed blood sent off for analysis

by nearby dedicated labs. Samples of the leukocytes are then taken to the flow cytometry

lab where a flow cytometer determines the number of leukocytes, hematopoietic stem

cells and their respective viabilities per milliliter.

A dedicated microbiology lab takes fungal and TSA (bacterial) plates from the cleanrooms

and incubates them to check for no bacterial or fungal contamination in the cleanroom

during processing and particle counters under the safety cabinets monitor the number of

particles present to guarantee no contamination of the medical samples. Biohazardous

samples (those from patients with AIDs, blood diseases or from high-risk countries) are

isolated into a separate Eterne and constantly logged, cross-checked and monitored

ensuring samples are up-to-date and all equipment in contact with them is recorded.

Results

Data was copied from hard copies dating from 03/01/2012 through to 15/10/2013 into a

database for analysis; a total of 798 records were entered. The fields of information

recorded included date received, unique references, incoming,

processing and final weight, viability, leukocyte count, CD34+ count,

birthing method, birth weight, collection date (DOB), maternal DOB

and gestation period. The volume of each sample was calculated by

deducting the weight of the emptied collection bag from the incoming

weight and assuming a density equal to that of water (1gcm-3). 218

records were partially incomplete in some way and these records have

been removed from relevant analysis and statistics.

The first hypothesis that was tested was that babies delivered after 40

weeks gestation period gave lower sample volumes with lower stem

cell counts and lower viability. The first sub-hypothesis of this was

that babies delivered post 40 weeks gestation give lower sample

Figure 2 Two sample Welch t-test between pre and post 40 week volume data sets

Figure 2 Distribution plot of volume data frequency by gestation week


volumes. 629 records gave full information for both the calculation of

volume and the gestation period combined. With the number of

samples in excess of 600, there is no requirement for the data to be

transformed (despite a slight left tail skew) due to the Central Limit

Theorum which stipulates 30/31 datum to be sufficiently large not to

require transformation.

Using statistical software Minitab, the results were analysed and a

two-sample T-test was performed on the datum below 39 weeks and

the datum above 39 weeks (Figure 2). The resulting T-value of 0.84

indicates a statistically significant difference between the two samples

to a 0.400 or 60% confidence (P-Value = 0.400). The scientifically

accepted level of significance required to allow the rejection of a null

hypothesis is 95%; hence this result is negative. There is no

discernable effect of post 40-week gestation on collected volume

against a pre 40-week gestational period. However, subsequently an

ANOVA (Analysis Of Variance) test was performed on the data below

40 weeks, equal to 40 weeks and above 40 weeks.

The ANOVA testing did not assume equal variances and all values are

reported to the accepted 95% certainty level (Figure 3). The test

reported an interval plot of the data. As visible (Figure 5), the test

presents a significant difference between the means of those

gestational periods outside of 40 weeks and those of 40 weeks. A

Games-Horwell Pairwise comparison test was carried out on the data

and reported that the mean for 40 weeks gestation was 95%

significantly different from that of both pre and post 40 weeks. The

interval plot presented shows this trend with an unexpected dip in the

mean for 40 weeks meaning that babies delivered after 40 weeks

gestation deliver lower umbilical cord volumes than those born earlier

or later. The data was split down into its components to acknowledge

any offset and an interval plot was created again (Figure 6). As seen by

the second interval plot, there is no real difference between the means

and the overlapping error bars make it such that the null hypothesis

may not be accepted.

To conclude: there is no statistical difference between the volumes

delivered by babies born before or after 40 weeks. Nor is there a

difference between the volumes delivered after 40 weeks or after

gestation periods outside of that. This concords with the findings of K

K Ballen (K K Ballen, 2000) whose results report no significant change

in volume as gestational period changes.

Figure 3 One-way ANOVA test on volume data sets pre, post and equal to 40 weeks gestation

period

Figure 4 Test for equal variance between volume data sets of pre, post and 40 week gestation

periods

Figure 5 Interval plot for volume grouped as pre, post and 40 weeks gestation period

Figure 6 Interval plot for volume grouped by gestation week

Figure 7 Test for equal variance between CD34+ count data sets of pre, post and 40 week

gestation periods


The second part of this hypothesis was that babies delivered after 40

weeks deliver lower stem cells counts. Stem cells are counted by the

presense of CD34+ fluorchromes detected in flow cytometry.

A critical part of these statistics are that the variance between

population groups is equal. To determine whether pre 40, 40 and post

40 week groups were of equal variance, an equal variance test was

performed on the groups. As shown by the test for equal variance

(Figure 7), because the error bars overlap, there can be no significant

difference in variance assumed. Therefore, equal variance may be

assumed and in any event, this small disparity in variance will have a

small affect on ANOVA or T-Testing.

ANOVA testing was performed on all pre 40 week, 40 week and post

40 week samples with Tukey pairwise, Fisher Pairwise and Dunnett

Multiple Comparison tests all reporting no significant difference

between the samples all to over 95% certainty. The null hypothesis

must be accepted in this case: there is no decline in CD34+ stem cells

in a sample when gestation period exceeds 40 weeks.

The final part of the first hypothesis was that gestation periods over 40

weeks give lower viabilities. Viability is measured by counting

flurochromes attached to exposed DNA which is released from dead or

comprimised cells. In this way, the viability of the sample can be

assesssed.

A test for equal variance was performed on the viabilites which yielded

a graph (Figure 8) stating that there is no significant difference in

variance and so ANOVA testing is viable assuming equal variance.

Tukey parirwise, Fisher pairwise and Dunnett multiple comparison

tests all yieleded results indicating that there is no significant

difference between viabilites of babies born pre 40 weeks or post 40

weeks (Figure 9).

To ensure no error in the findings, the groups were split into

component weeks. The graph (Figure 8) shows the overlapping

variance error bars indicating that equal variance may be assumed for

further tests. ANOVA testing was then carried out (Figure 10), the

results of which gave Tukey pairwise, Fisher pairwise and Dunnett

mulitple comparison test results supporting no difference between the

individual samples.

Figure 8 Test for equal variance between viability data grouped by gestation week

Figure 9 Tukey, Fisher and Dunnett tests on

viability data sets grouped into pre, post and

equal to 40 weeks gestation

Figure 10 Tukey, Fisher and Dunnett tests on viability data sets grouped by gestation week


In conclusion, the first hypothesis cannot be accepted and the data

does not support the idea that gestation periods in excess of 40 weeks

lead to lower viability, volume or stem cell counts.

The second hypothesis was that increasing maternal age correlates

with a decline in viability, volume and CD34+ count. Firstly, the CD34+

count equal variance test was performed. The report (Figure 11) shows a

multiple-comparisons p value of p=0.390 (61% confidence) which is

greater than the accepted value of p=0.05 required to reject the null

hypothesis; for this reason, the null hypothesis must be accepted and

therefore we can safely assume that all variances are equal.

ANOVA testing was performed and as the homegenity of variance

assumption is valid, equal variance testing could be performed. Tukey,

Fisher and Dunnet multiple comparison tests all returned no

significant difference between the means of the intervals and an

interval plot revealed no significant disparities between any groups

(Figure 12).

Although the interval plot (Figure 13) seems to draw a curve based on

the mean points, as a single straight line could be drawn through all

intervals’ error bars, it cannot be assumed that there is any significant

correlation between increasing maternal age and CD34+ count.

The data is also exhibits a large spread and although the p value for a

signficant difference is 58.1%, the R2 is merely 0.54% and 0.00%

adjusted. This miniscule R2 value indicates that the model’s regression

line fits just 0.54% of the data which further casts aspersions over any

correlation seen. It must be accepted that there is no statistically

signficiant difference between the means and therefore the null

hypothesis must be accepted. There is no correlation between

increasing maternal age and a decline in CD34+ count.

Figure 13 Interval plot of CD34+ data grouped into 5 year intervals

Figure 11 Test for equal variance between CD34+ data sets grouped into 5 year age groups

Figure 12 ANOVA testing on CD34+ count grouped into 5-year intervals


The second part of this hypothesis is that increasing maternal age

correlates with a decline in incoming volume. A volume variance

check was performed and, as presented in figure 14, the p value of

p=0.782 (21.8% confidence) means that the data cannot be assumed to

have unequal variance. This is supported by the overlapping error bars

seen in the equal variance graph which indicates that none of the data

sets are of disparate variance.

Again ANOVA testing was performed on the data set along with a

Tukey, Fisher and Dunnet multiple comparison test. The results of the

ANOVA testing are presented in figure 15. The analysis of variance

reported a p value of p=0.005 (99.5% confidence) which indicates that

there is a significant difference in at least one mean across the data set,

meaning there may be a possible correlation. Tukey pairwise

comparison was conducted under 95% confidence statistical

standards. Tukey pairwise comparison reported two different letters

across the data set with post 45 years being the highest mean group,

ages 35-45 being grouped into a second mean group (not significantly

different to the first group or the third) and ages under 35 are grouped

into a third lower mean group (significantly different in mean from the

over 45 group). The Tukey results can be read as a sliding scale with a

gentle but signficant decrease in mean volumes occuring as age group

age decreases. This test points towards a positive correlation between

maternal age and volume – a direct contradiction of the initially

proposed hypothesis.

Fisher pairwise comparison reported the same trend. The Fisher test

split the data into groups >45 having the highest mean, group 40-45

having a lower mean neither significantly different from >45 nor pre

35-40. Group 35-40 was assessed as signficantly different from group

>45 but not from group 40-45 or 30-35. Groups 30-35 and <30 were

judged to be statistically different from group 40-45 and group >40 but

not from group 35-40.

The Fisher pairwise comparison can be read similarly to the Tukey

comparison as a sliding scale with >45 the highest mean through to

<30 with the lowest. The Fisher pairwise comparison was conducted to

95% confidence level and again points towards a postive correlation –

not negative - between maternal age and volume.

Figure 14 Test for equal variance on volume data grouped by maternal age in 5 year intervals

Figure 15 ANOVA testing on volume data grouped by maternal age in 5-year intervals


With a significant trend identified, a residuals against fit

graph was created to assess any obvious sources of bias or

inaccuracy statistcially. As seen in Figure 16, the residuals are

not wildly different in terms of height or spread. The

residuals tend to ‘bounce randomly’ about the 0 line which

suggests the relationship is linear and fair to make. The tops

and bottoms of the residual plot forms a rough horizontal

band about 75 and -50; this suggests that the variances of the

error terms are equal.

The Fisher plot in Figure 17 shows that there are clearly

signficant differences between groups. When reading a Fisher

plot, if an interval does not pass through the zero line then

there is a significant difference between the interval’s groups’

means. There is a clear difference between groups 35-40 and

<30, a greater difference between groups >45 and <30 and a

significant difference between groups >45 and 35-40. This

concords with the findings of the Fisher statistical test and

provides further support for the correlation between

maternal age and volume.

An interval plot was created (displayed Figure 18) which

presents the apparent upward trend with increasing group

age and increasing volume. Error bars on the interval plot in

figure 18 are calculated from the pooled standard deviation

values. The means are plotted from values calculated and

adjusted to 95% certainty of the real values. Although

sequential error bars overlap to high levels in instances, there

is a separation between the pre 30 group and the post 45

group which indicates that the trend line may be correct.

A normality probability plot (Figure 19) was also made to

assess the normality of the data. A normality probability plot

is used to see whether a data set is roughly normally

distributed or not. The 45° normal line is visible and most of

the data falls approximately along that line. As with almost all

biological samples, there are outliers and the data does curl away from the normal line at

either end which is not unexpected for data of this type. If the data set were wildly

different from the normal line then this would be a cause for concern in the reliability of

these results however with such a large sample (n=729), the Central Limit Theorum safely

covers any issues with normality in this data.

Figure 16 Residual versus fit plot for volume data by maternal age

Figure 17 Fisher inter-group plot of volume data grouped by maternal age in 5-year intervals

Figure 18 Interval plot of volume data grouped by age in 5-year intervals

Figure 19 Normality probability plot of volume data against line of normality


To summarise, there is a statistically significant possitive correlation

between maternal age and the volume of cord blood collected to a

degree of confidence in excess of 95%.

The final part of the second hypothesis is that as maternal age

increases, viability of stem cell samples decreases. The first action was

to check the data for equal variance and the report (Figure 20) came

back with p=0.000 (100% certainty) that at least one of the 5-year-

interval groups was not of equal variance. This is furthered by the

graphical plot. When reading the graphical plot, if two intervals do not

overlap then the corresponding standard deviations are signficiantly

different and the variance may not be considered equal. As seen in

Figure 20 not all intervals do overlap; for example, the interval of

group ‘PRE35’ (30-35 years old) does not coincide with the interval of

‘POST40’ (40-45 years old) at any point. Therefore the data must be

considerd unequal which limits the number of statistical tests which

may be performed.

Despite this, the Levene value for this data is p=0.032. The Levene

value is the measure of whether the variance disparities between the

data could have arisen through random chance in a truly equally

variated sample. The fact that the Levene confidence is under p=0.05

(96.8% confidence) does indicate that there is a real difference

between the ages and any correlation will be more significant as it is

highly unlikely to have occurred due to random chance.

ANOVA testing was then conducted based on the assumption that the

variances of the data were unequal and the report is shown in Figure

21. Welch’s test gave a p value of p=0.000 (100% confidence) in at least

one mean being statistically significantly different. Subsequently a

Games-Howell pairwise comparison was conducted to the

scientifically accepted 95% confidence degree. The result is that the

test judged certain means to be disparate from others. Group >45 was

the highest mean in one group, group 40-45 was neither significantly

different from group >45 or group 35-40. Group 35-40 was not

signficantly different from group <30 and shared a letter with group

<40-45 however all three groups were judged to be significantly

different from group >45. Group <35 was significantly different from

groups 40-45 and >45. The Games-Howell results may be read as a

sliding scale from group >45 having the highest mean to group <35

having the lowest mean value. The plot of the Games-Howell data,

presented in Figure 22, shows the significance between each group.

Figure 20 Test for equal variance between viability data grouped by maternal age in 5 year intervals

Figure 21 ANOVA testing on viability data grouped by maternal age in 5 year intervals

Figure 22 Games-Howell plot of viability data grouped by maternal age in 5 year intervals


When reading a Games-Howell plot, if an interval does not

pass through the zero line then the respective groups are

significantly different from oneanother. Evidently, groups

<30 and >45 are significantly different, as well as groups 30-

35 and 40-45, groups 30-35 and >45 and groups >45 and 35-

40.

After noting the correlation, an interval plot was

constructed to assess the true trend of the data (displayed

Figure 23). The interval plot uses individual standard

deviations to calulate error bars and the 95% confidence

calculated true mean for the central mean plot point. The

trend of the data presents a clear positive correlation

between increasing age and increasing viability however.

Although sequential error bars overlap, there are evident

discernable disparities between >40 year maternal age

viabilities and <35 year maternal age viabilities. A normaility

plot was created to assess the normality of the data and as

presented in Figure 24, the data is not very normal as it does

not follow the 45° normality line very true. However, the

Central Limit Theorum states that for samples over 30/31

(n=31), the normality of the data is not an issue due to the

sheer size of the data set and in this experiment n=729.

With a significant trend identified, a residuals against fit

graph was created to assess any obvious sources of bias or

statistcial inaccuracy. As seen in Figure 25, the residuals do

vary in terms of height or spread. The residuals of the first

three groups tend to ‘bounce randomly’ about the 0 line however the last >45 group

suffers particularly from a shorter residual plot and a lower spread which is mainly due to

the lower sample size for viability by maternal age >45 years. The tops and bottoms of the

other residual plots form a rough horizontal band about -0.25 and 0.15; this suggests that

the variances of the error terms are loosely equal. There are quite a few outlying residuals

however with such a large sample size, this is to be expected.

To conclude, the alternate hypothesis was that samples from mothers with a higher

maternal age give lower volumes, CD34+ counts and lower viability. The null hypothesis

was that samples from mothers with a higher maternal age give the same volumes, CD34+

counts and the same viabilities. The alternate hypothesis must be rejected but so too must

the null hypothesis. The proposed hypothesis from these findings follows: samples from

mothers with a higher maternal age give higher volumes, the same CD34+ counts but with

higher viabilities.

Figure 23 Interval plot of viability data grouped by maternal age in 5 year intervals

Figure 24 Normality probability plot of viability data by maternal age

Figure 25 Residual versus fit plot of viability by maternal age


Whilst inputting the data, a trend was noted between higher birth

weight and higher CD34+ counts. I constructed a new null hypothesis

that there is no difference between CD34+ as birth weight rises with

the alternate hypothesis that as birth weight rises, CD34+ count rises.

A variance test was performed to assess whether equal variance could

be assumed across the entire data. The results of the equal variance

testing are displayed in Figure 26 and as reported, the probability that

at least one mean is different came out as p=0.621 (37.9% confidence)

and therefore it can be safely assumed that all variances in this data

set are equal. The corresponding equal variance interval plot also

concurs with this result. When reading an equal variance test plot, if

two intervals vertically overlap then their means are not significantly

different and as all intervals vertically overlap with one another, the

variance may be considered equal for ANOVA testing purposes.

ANOVA testing was then perfomed to a confidence level of 95% using

the assumption that all variances are equal. The report is presented in

Figure 27. As shown in the report, the probability that at least one

mean is different and the null

hypothesis may be rejected at is

p=0.014 (98.6% confidence). Tukey

analysis gave the results as a gentle

sliding scale from birth weights

above 3700g having the highest

mean and being significantly

different from those with the lowest

mean which was the under 2900g

group. Fisher pairwise comparison

reported the same trend that there

was a sliding scale of means from

the highest >3700g to the lowest

<2900g with significance between

the two but not particularly

between the groups between these

two means.

Dunnet multiple comparisons test

was conducted with the

middlemost group (3300-3499g) used as the control value. There was

no signficant difference reported between the 3300-3499g group and

any other group however as the middle group, this concords with the

results of the Tukey test that there is no signficant difference purely

because this is in the middle of two signficantly different means.

Figure 26 Test for equal variance on CD34+ count data grouped by birth weight (grams)

Figure 27 ANOVA testing on CD34+ count grouped by birth weight in 200g intervals

Figure 28 Interval plot of CD34+ count data grouped by birth weight in 200g intervals

Figure 29 Residual versus fit plot of CD34+ count data grouped by birth weight in 200g intervals


An interval plot was constructed in order to assess the true trend of the data and to see

whether the trend was strong or weak. The plot is displayed in Figure 28 where the pooled

standard deviations have been applied as error bar values. The relative trend of this data is

clear in that there is a strong iterative increase in CD34+ count as birth weight increases.

Again, although most sequential error bars overlap, the true trend is evident and the two

end means have no overlap in their error bars.

To assess whether there was any obvious statistical bias or fault in the data set, a residual

against fit plot was made (Figure 29). The data appears to be randomly spread about the

0-line with a roughly equal amount either side, no obvious peaks or troughs wildly

differing from any other group with a roughly horizontal banding occuring from 50 to -25.

There are two notable outliers and one is fairly severe although with the sample size of

every group being equal or above n=98, this should not have too great an effect on the

results.

In conclusion, we can safely reject the null hypothesis that there is no difference in CD34+

count as birth weight increases and can accept the alternate hypothesis that as birth

weight increases, so too does CD34+ count. This result is concordant with the findings of

KK Ballen of the University of Massachusetts Cancer Centre who determined that every

500g increase in birth weight contributed to a 28% increase in CD34+ cell counts. (K K

Ballen, 2000).

DISCUSSION

Despite the collected data supporting the hypotheses that older maternal age gives higher

volumes, higher viabilities and that babies with greater birth weights give higher CD34+

counts; reports have presented the fact that the true success of cord blood stem cell

transplantation relies on the number of nucleated cells transplanted and the volume

collected (Will, 1999).

Another factor, not accounted for in this investigation is the recipient’s weight. As John

Wagner’s paper reports (Wagner, 2002), when HLA blood types are no more than 2

leukocytes disparate then there is a high probability of survival in recipients of UCB grafts

when the grafts contain at least 1.7 x105 CD34+ cells per kilogram of recipient’s body

weight. Given that the recipient’s weight is an uncontrollable factor in the delivery of the

cord blood, the CD34+ count itself must be considered vital in the success rate of the cord

blood transplant. Therefore, birth weight is crucial to the success of the storage of any

stem cell samples. Unfortunately, no statistical link could be found between maternal age

and birth weight but gestation period will have an impact on the birthing weight.

Very little explanation can be given for the detected correlation between maternal age and

viability or volume although a few suggestions have been proposed as reasons for these

links. Perhaps older mothers are less likely to be first-time mothers and mothers who have


previously given birth experience less trauma and stress to the umbilical cord cells during

labour and delivery. Alternatively, it has been suggested that lifestyle of older women is

likely to be less erratic and healthier with fewer chemical and physiological instabilities as

are present in younger mothers. This could lead to a less traumatic and a more stable

pregnancy which may have an effect on the quality of cells at birth.

In regards to the finding of no correlation between gestation period and viability, cell

count or volume, the findings seem to, again, contradict common literature supporting

the idea that gestation periods over 40 weeks are unhealthier pregnancies due to the

breakdown of elements of the womb after this period which can risk stillbirth or birthing

complications (the reason for induced labour options being recommended after 42 weeks

gestation) (NHS, 2015).

This report concludes that mothers giving birth to babies at a maternal age over 35 will

deliver samples of greater volume and stem cell viability. The number of CD34+ cells per

microlitre increases with birth weight which is a key factor for the success of future

transplantations. This said, the purity of the umbilical cord blood (most births result in

leakage of blood from mother to baby) and the presence of cord blood stem cells in favour

of maternal red blood cells is also of significant importance in many factors of storage

from freezing and preservation to transplantation and viability (M-Reboredo, 2000). A

proposed extension of this report is in collecting and collating data on whether mothers of

the newborn are first-time mothers or not, how many hours of labour the mother went

through and the weight of the mother at time of birth to assess the effect of these factors

on the data.


References Ghadeer Ibrahim Alrefaei, S. A.-K. (2015, August). Does the maternal age affect the mesenchymal

stem cell markers and gene expression in the human placenta? What is the evidence?

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