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Cervical Cancer is a preventable disease. Sadly it is a cause morbidity and mortality of women in poor socio-economic settings worldwide, largely due to avoidable factors. This amounts to a gross violation of the rights to life and access to healthcare of vulnerable populations of women. Awareness, sensitisation and mobilisation of the civil society could be crucial in influencing change in political will and healthcare policies to address the needs of HIV infected women, in particular, given their susceptibility to the development of cervical cancer. This work advocates for the South African government to further commit resources towards the prevention of cervical cancer, including hysterectomy in women at high risk of developing cervical cancer before it ensues. / Mini Dissertation (MPhil)--University of Pretoria, 2019. / Public Law / MPhil / Unrestricted

Studying lymphocyte development using mass cytometry Abstract Development of mature lymphocytes, a white blood cell subtype, is crucial for the correct function of the human immune system. Currently, developmental pathways of lymphocytes can be studied using high-throughput single-cell measurements. In particular, mass cytometry enables the study of immunologically relevant pheno- typic and functional markers on a vast scale. In this work I present my individual contribution to tviblindi, a powerful software tool for analysis of cytometric data aimed at uncovering developmental trajectories. tviblindi is a package written in R, Python and C++. It provides a means to integrate prior knowledge with data analyses grounded in graph theory and algebraic topology. tviblindi is accessible to biological researchers without background in computer science or mathematics. It is an addition to the expanding field of trajectory inference in single-cell data. Furthermore, I review current knowledge of T-cell development and conduct a tviblindi analysis thereof using human thymus and peripheral blood datasets and evaluate the results. 1

We propose a model that generates a family network based on real-world family network data. We use this model to study the extent to which distances to union and the number of children characterize family networks. To determine how accurate our model is we use persistent homology to identify and compare the structure of our modeled family networks to real-world family networks. To accomplish this, we introduce the notion of a network's persistence curve, which encodes the network's set of persistence intervals. Using the bottleneck distance allows us to measure the difference in the homological structure between any pair of networks. We also study how the distribution of distance to union and the distribution of children build family networks. What we find is that these two features of distance to union and number of children allow us to fairly accurately recreate family networks at least at the level of their persistent homology.

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BACKGROUND: The prevalence of Alzheimer’s disease (AD) and other types of dementia is expected to drastically increase between now and midcentury because of the aging baby boomer population. It is projected that by 2030, 74 million people aged 65 and older will comprise nearly 20% of the population (United States Census Bureau, 2017). By 2060, it is estimated that about 13.8 million people will have AD. In 2020, the estimated total health care costs for treating individuals with AD in 2020 is $305 billion (Wong, 2020). As the population ages, the cost is expected to increase to more than $1 trillion. This significant economic and health care burden could be greatly alleviated by the development of a treatment that would delay the onset of the disease or prevent the disease altogether. Increasing evidence supports cardiovascular health being linked to the health of the brain. Diabetes is a particular risk factor that increases the likelihood of cardiovascular disease and is consequently associated with a higher risk of developing AD and other dementias. AIM: The aim of this study was to determine the association between persistent high blood glucose during midlife to late life and the risk of incident dementia and AD. METHODS: This study included 1287 Framingham Offspring participants (669 women, mean age 68.6 ± 5.7 years) who were free of dementia and attended 5 consecutive examinations at 4-year intervals starting at midlife (Exam 3: 1983–1987, mean age 54.6 ± 5.8 years) until late life (Exam 7: 1998–2001, mean age 68.6 ± 5.7 years). These participants were subsequently followed up for incident dementia after a period of time (mean 14 ± 4.5 years). Based on the resulting data, this study examined the effect of midlife diabetes (fasting blood glucose level ≥ 126 mg/dL), late-life diabetes, 10-mg/dL incremental increases in fasting blood glucose (FBG), persistence of diabetes during midlife to late life, and a steep increase in FBG from midlife to late life over an 18-year exposure period. Further stratified analysis was completed on a subgroup of participants with a steep incline in FBG to determine if there was an interaction effect with apolipoprotein E4 (APOE4) carrier status. RESULTS: During the follow-up period, 172 participants developed dementia, and of these cases, 135 participants had AD. Multivariable Cox proportional hazards models showed that persistent high FBG was associated with greater than 2-fold increase in risk of both incident dementia (hazard ratio [HR] 2.13, 95% confidence interval [CI] 1.37-3.33) and AD ([HR] 2.18, 95% [CI] 1.33-3.57) after adjusting for age, sex, education, APOE4, prevalent cardiovascular disease (CVD), and midlife diabetes treatment. In addition, individuals who experienced a steep increase in their FBG from Exam 3 to Exam 7 were associated with an increased risk of developing AD (p value = 0.022). Further stratification by APOE4 carrier status with a steep increase in FBG revealed that APOE4 non-carriers were associated with an approximate 2-fold increased risk for developing incident dementia ([HR] 1.90, 95% [CI] 1.12-3.16; p value < 0.05) and AD ([HR] 2.30, 95% [CI] 1.28-4.06; p value < 0.05). CONCLUSIONS: Persistent high blood glucose was associated with an increased risk for developing incident dementia and AD in a community-based cohort. A steep increase in FBG during midlife to late life also increased the risk for developing dementia and AD in this cohort. These data support the potential of sustained cognitive benefits from lower blood glucose levels in midlife but also suggest that sharp increases in blood glucose levels in older adults may be a risk marker for dementia and AD.

Fatigue is a common problem among cancer patients, especially those who have received chemotherapy and radiation therapy. Stem cell transplant (SCT) patients are at a particular risk of persistent fatigue as they receive more aggressive therapies. This study examined the prevalence of fatigue after completion of SCT. Further, the level of fatigue symptom distress and its relationship with quality of life (QOL) among long term SCT survivors was examined. The study involved thirty-three patients, 21 males and 12 females, treated with autologous or allogeneic SCT in a comprehensive cancer center in Southwest Florida. Participants' ages ranged from 36 to 70 years, with a mean age of 53 years. All subjects completed the Cancer Related Fatigue Distress Scale and the Functional Assessment of Cancer Therapy-Bone Marrow Transplant questionnaires. All the patients had to be at least six months from transplant. The results of this study showed that fatigue is quite prevalent among SCT survivors. Ninety-three percent of the patients reported some degree of fatigue, and 15% experienced severe fatigue. Patients who received autologous transplant (24%) reported less fatigue symptom distress (mean= 48, SD= 36.62) compared to the allogeneic transplant group (mean= 66.2, SD= 54.49). A strong negative relationship was found between fatigue symptom distress and QOL (r = 0.85, p < 0.0001) suggesting that patients with the greatest fatigue distress report the worst QOL. The time from transplant factor was significantly positively associated with fatigue symptom distress (r= 0.46, p= 0.007) indicating greater distress with the passage of time. A moderate negative relationship was also found between time from transplant and QOL (r= -0.34, p= 0.052) suggesting that QOL was less in some patients as time passed; however this was a weak relationship that did not achieve statistical significance. Although the sample size was small, this study was able to provide a confirmation that fatigue symptom distress and QOL are related to one another. Understanding the relationship between fatigue symptom distress and QOL should encourage interdisciplinary collaboration in planning proper interventions to minimize fatigue. This would improve the outcomes of SCT long term survivors, and would positively impact their overall QOL.

A ciliate is a phylum of protozoa that has two types of nuclei, macronuclei and micronuclei. There may be more than one of each type of nucleus in the organism [1]. The macronucleus is the structure where protein synthesis and cell metabolism occur [1]. The micronucleus stores genetic information and is mobilized during a sexual reproduction process called conjugation [1]. The somatic macronucleus (MAC) is developed from the germ-line micronucleus (MIC) through genome rearrangement during a sexual reproduction process called conjugation [6, 8]. Segments of the MIC that form the MAC during conjugation are called macronuclear destined sequences (MDSs) [8]. During sequencing each MDS is given coordinates where the MDS sequences begin and end in the MIC. The orientation of a MDS in the MIC can be taken to be positive or negative. If the direction of the MDS in the MIC agrees with the direction in the MAC then the orientation is positive otherwise it is a negative orientation. In this thesis we analyze various aspects of the gene assembly during the rearrangment process of the ciliate Oxytricha trifallax that were recently sequenced [15]. Some of the properties analyzed include overlapping MDSs, orientation, MDSs starting and ending position in the MIC and the gaps of overlapping MDS pairs. A gap of an overlapping MDS pair is the order difference of two MDSs for a particular MAC contig that overlap in the MIC contig. We use 120 MAC contigs from [15] that have overlaps among their own MDSs. These 120 MAC contigs make up the data set we call D4. We explore the patterns of overlapping MDSs in the MIC in D4. To quantify such patterns, we associate a vector V (An) to each MAC contig An, where V (An) = (v1(An), v2(An), v3(An)) is a vector in R3. The first entry is the number of overlapping MDS pairs divided by the number of MDSs. The second entry is the sum of gaps of overlapping MDS pairs divided by the sum of all possible gaps. The final entry is the total number of overlapping base pairs divided by the total length of the MAC contig. We computed the distance matrixM = (dij) where dij is the Euclidean distance between V (Ai) and V (Aj). The MAC contig vectors and M were computed using Python. To analyze D4 we applied Topological Data Analysis (TDA). TDA uses topological constructs to assess shapes in data [3, 12]. From the data entries of the distance matrix M = (dij) we applied a Vietoris-Rips filtration to generate the barcodes of the persistent homology in dimensions 0, 1 and 2. The persistence barcode of 0-dimensional homology illustrates clusters of the data while the 1-dimensional homology represents non-trivial loops in the simplicial complex [3, 13]. The application of TDA on the ciliate Oxytricha trifallax identified ten MAC contig clusters at epsilon= 0.1 in D4 and several loops that were persistent for two or three epsilon values. Other TDA methods can be applied to the Vietoris-Rips filtration to further identify which MAC contigs appear in each cluster.

Studying lymphocyte development using mass cytometry Abstract Development of mature lymphocytes, a white blood cell subtype, is crucial for the correct function of the human immune system. Currently, developmental pathways of lymphocytes can be studied using high-throughput single-cell measurements. In particular, mass cytometry enables the study of immunologically relevant pheno- typic and functional markers on a vast scale. In this work I present my individual contribution to tviblindi, a powerful software tool for analysis of cytometric data aimed at uncovering developmental trajectories. tviblindi is a package written in R, Python and C++. It provides a means to integrate prior knowledge with data analyses grounded in graph theory and algebraic topology. tviblindi is accessible to biological researchers without background in computer science or mathematics. It is an addition to the expanding field of trajectory inference in single-cell data. Furthermore, I review current knowledge of T-cell development and conduct a tviblindi analysis thereof using human thymus and peripheral blood datasets and evaluate the results. 1