MSc.BM Theses and Dissertations
Permanent URI for this community
Browse
Browsing MSc.BM Theses and Dissertations by Issue Date
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- ItemTransmission of HIV partly depends on the relative population of, In-host, wild-type versus resistant susceptible lymphocytes: a study of sexually active females(Strathmore University, 2017) Cole, Andrew OmandiAbout 240 people on earth contract HIV every hour according to the American Foundation for AIDS Research. Many researchers have put in millions of man hours in the quest to identify viable targets for identification and management of the viral infection. In this dissertation, an attempt is made to use available molecular data to identify unique genetics finger print profiles that can be used to reliably predict those at risk of HIV acquisition. Such unique genetic fingerprints could also be targets for future development of vaccines that may help in the quest for winning the fight against infection in the first place. Most laboratory kits for HIV will reliably identify those people already infected with the disease and a test that can identify those at risk or those that could benefit from future vaccination is timely. Such a discovery could go a long way in mitigating the tremendous effects of HIV infection on individuals and societies at large. My findings from the analysis show a good number of differentially expressed genes with the potential to build a reliable predictive model for HIV acquisition. More studies that are prospective in nature need to be conducted to further illuminate and characterize these potentially helpful findings.
- ItemMathematical modelling of the efficacy and toxicity of cancer chemotherapy(Strathmore University, 2017) Osotsi, John IndikaFrom available literature, there is strong evidence that the growth of tumours is to a great extent influenced by the cellular response of the immune system in addition to the therapy administered. While chemotherapy treatment is very effective in killing cancer cells, the levels of toxicity associated with it affects other body cells negatively, the worst of which are cells with higher rate of multiplication and regeneration. A lot of research, with impressive results has been carried out in cancer for the past over four decades, yet there is still not a universally accepted effective mathematical model that provides a way of optimizing chemotherapy efficacy and toxicity.The mathematical model developed in this research has provided a theoretical understanding of the interactions among cancer cells and body cells for cancer patients as well as laying the stage for future research work. Based on the findings from reviewed biological literature, a mathematical model comprising of six ODEs describing the growth of tumour cells while incorporating the immune system response and chemotherapy treatment was formulated and analyzed both analytically and numerically. Three scenarios are presented namely: no tumour with no treatment, tumour with no treatment and tumour with treatment. In the first case (no tumour and no treatment), the system was found to be stable. The tumour with no treatment equilibrium was on the other hand was found be unstable implying that the immune system cannot eliminate cancer cells on their own.Lastly, the case of tumour with treatment was found to be stable hence longer survival times for the patients receiving chemotherapy treatment. When however, the concentration of chemotherapy was increased, the system goes back to instability due to the decline of the number of NK and CD8+ T-cells as a result of chemotherapeutic toxicity.According to the results of the formulated mathematical model, treatment regimens consisting of right concentrations of chemotherapy is effective in eliminating the tumour cell population. Further research should therefore focus on developing models that quantify the optimal drug concentration for maximum efficacy on tumour cells with minimal toxicity to immune cells.
- ItemCOVID-19 viral - host interaction dynamics and immune response(Strathmore University, 2022) Kaumbutha, Charles MwangiEven as the world eases into the COVID-19 pandemic, new variants of the virus keep sprouting and destabilizing normal routines. Initially, to arrest the high transmission of SARS CoV-2, non-pharmaceutical approaches were utilised simply for damage control as other interventions such as development of vaccines were being explored. Fortunately, the urgency to control the crisis prompted the vaccine development process to be expedited. Concurrently, Food and Drug Administration (FDA) approved the use of Remdesivir to treat specific demographics of COVID-19 patients. Despite these measures, progressive studies are still providing fresh knowledge. Quantitative approaches have therefore provided useful insights in understanding key aspects of SARS CoV-2. We developed and analysed a mathematical model to describe the evolution of the virus, its interaction with immune cells, importance of immune response and potential targets for drug development. The well-posedness of the model was determined based on positivity and boundedness of solutions. The model suggests that a greater efficacy of immune cells significantly reduces the viral load. Further, numerical simulation suggests that inhibiting the progression of latently infected cells to productively infected cells is paramount and this can be achieved by using viral transcriptase inhibitors. We suggest that these inhibitors together with the use of approved vaccines and re-purposed antiviral drugs such as Remdesivir and Baricitinib will have a great impact in controlling the severity of the virus in case of subsequent attacks. COVID-19 vaccines confer immunity by primarily utilising the SARS CoV-2 spike proteins. We evaluated the impact of immunization on target and infected cells. Results obtained from the simulations indicate that a lower vaccine efficacy requires booster shots to augment circulating antibodies necessary to nullify the virus. This model of in-host SARS CoV-2 dynamics has therefore provided knowledge useful to encourage the development of antiviral drugs focused on inhibiting transcription hence arresting viral replication.
- ItemModelling depression treatment and HIV care cascade dynamics in Kenya(Strathmore University, 2023) Chemutai, J.HIV/AIDS has become one of the major global health burdens and threat to public health. By the end of 2021, 38.4 million people globally were living with HIV and over 1.4 million people live with HIV in Kenya. The “HIV care cascade” serves as an individual-level tool for evaluating HIV care and treatment results and a population-level paradigm for estimating the percentage of HIV-positive individuals in a given region who are participating in each subsequent phase. Several factors have been highlighted to influence the HIV care cascade and among this is depression which influences the improvements in ART service provision; diagnosis of people living with HIV and AIDS (PLWHA), linkages to care, continued engagement in HIV care and retention in HIV care which are crucial in attaining the 95% on ART target in the sub-Saharan region. Thus, This study employed mathematical compartmental modeling to investigate the impact of depression treatment on the HIV care cascade dynamics in Kenya. A deterministic compartmental model of the depression and HIV care cascade was developed from a system of Ordinary Differential Equations (ODEs). The basic reproduction number was evaluated using the next generation matrix. The numerical results showed that improving depression treatment can positively influence the HIV care cascade, leading to improved outcomes, such as higher rates of testing, linkage, adherence, retention, and viral suppression. The study highlights the importance of integrating depression treatment into HIV care services and provides valuable insights for policymakers and healthcare providers on how to improve the HIV care cascade dynamics in Kenya.