http://hdl.handle.net/123456789/995 2026-04-09T23:34:06Z http://hdl.handle.net/123456789/1102 : Exposure to bisphenol a analogues in non-human primates as a potential cause of endometriosis. Apiyo, O Introduction: Bisphenol A is a synthetic compound widely used in the production of polycarbonate plastics and epoxy resins worldwide. As an environmental toxin, it has been reported in plastic equipment and utensils, water bottles and bottle tops, water supply pipes and epoxy resins that coat most of the metal food cans. It is a known endocrinedisrupting chemical and has been progressively replaced by its derivatives including bisphenol S, bisphenol F, bisphenol E, bisphenol AF, bisphenol B and tetramethyl bisphenol F. Bisphenol A and its analogues can bind to estrogen receptors and trigger multiple cellular responses at the organism level. Methods: A comprehensive literature review was done utilising electronic databases of PubMed, Google Scholar, Hinari, Connected papers and Science Direct from 1991 onwards. The articles were only included if they reported original relevant research and were limited to articles written in English. Results: Animal models, including non-human primates, have been used to study their effects on the endocrine system. Its endocrine disruption activity is reported to be the most studied effect in reproductive biology indicating that it may potentially cause endometriosis in females. Though non-human primates are closely related to humans, limited data exists on their associations between Bisphenol A exposure and its analogues and the pathophysiology of endometriosis. Conclusion: Given the current multifaceted knowledge/theory on endometriosis etiology, there is a strong necessity to conduct further biomedical research that utilises non-human primates to study the link between endocrine-disrupting chemicals and its effects on endometriosis 2024-01-01T00:00:00Z http://hdl.handle.net/123456789/1101 Big data analytics for sustainability of SMEs' performance after COVID-19 pandemic in Kenya Musyoka, C The majority of a nation's wealth comes from small and medium-sized businesses (SMEs), which account for half of all jobs and 90% of all businesses worldwide. However, SMEs continue to face limited credit availability from suppliers, who also face liquidity problems, low sales, and high default rates. Advance reimbursement, the weight of punishments on postponed credit installments and other repeating costs have made the SMEs much more helpless. Big Data Analytics for sustainability of SMEs' Performance in Kenya after COVID-19 pandemic was the goal of the study using data analytics and data science. The study's theories are Complex Adaptive System and Strategic Choice Theory. Descriptive survey design was used in the study by the researchers. In each subcounty of Nairobi, the study focused on SMEs in Eastlands, Dagoretti North, Dagoretti South, Langata, Kibra, Roysambu, Kasarani, Ruaraka, Embakasi South, Embakasi North, Embakasi Central, Embakasi East, Embakasi West, Makadara, Kamukunji, Starehe, and Mathare are Nairobi County. In each SME, managers of operations, finance, customer relations, and supply chain was surveyed to obtain data. The Krejcie and Morgan tables was used to gather the 222 respondents for the target population. For the purpose of data analysis, descriptive statistics such as frequencies, percentages, mean, and standard deviation were utilized. The relationship between the variables was determined using multiple regulation and Pearson correlation. Tables, charts, and graphs were used to present the data. The study sought to determine if Data Science has an impact and it was established that, Business Intelligence has a mean of 3.9 (std. dv = 0.851) and Machine Learning a mean of 3.7 (std. dv = 0.928), average mean of 3.8 (std. dv = 0.8895) and all have a positive impact. The study also sought to determine if Data Analytics has an impact and it was established that, Predictive Analytics has a mean of 3.73 (std. dv = 0.850) and Prescriptive Analytics a mean of 3.85 (std. dv = 0.684), average mean of 3.79 (std. dv = 0.767) and all have a positive and significant influence on the Sustainability of SMEs' Performance in Kenya after COVID-19. The SMEs have ability to solve many unforeseen challenges in competitiveness through Data Science drivers such as Business Intelligence and Machine Learning. They were also able to implement legal framework that protects data on Data Analytics on Predictive and prescriptive analysis on the improvement of SMEs performance, survival and growth. 2023-03-01T00:00:00Z http://hdl.handle.net/123456789/1097 Deontological versus Teleological Views of Deception in Medical Research Lagat, D The need for valid research remains urgent and a priority for all authentic studies. One of the research procedures(allowed in special circumstances)isthe deception of research participants for some reasons in order to use them in the study. Questions emerge on whether deceiving participants is acceptable, and how deception actually compromisesthe respect for persons. Arguments for or against deception in research usually anchor on either deontological or teleological reasons. This however creates tension. This study has suggested determination on whether deception in research is right or wrong, based on how those studies balance concern for science versus researchparticipants’welfare. The most authentic study, as proposed in this paper, is a research approach which only accepts to use deception in research, when both the concern for science and concern for participants remains simultaneously high; and not deception at the expense of the research participants 2024-01-01T00:00:00Z http://hdl.handle.net/123456789/1095 Distribution of Fingerprint Patterns in A Population in Western Kenya Lunani, M; Wabale, M; Munyekenye, G; Kenny, K Fingerprint is one of the oldest and most reliable biometric tools taken as legitimate proof of identification of an individual. Gender, ethnicity, and relationshipstatus are used as tools of individual identity and their associations with finger-print patterns have been demonstrated in previous studies but were inconsistent. The objective of this study was to describe the distribution of fingerprint patterns in a population in Western Kenya. The specific objectives were to determine the associations between finger print patterns and sub-patterns with fingers, gender, ethnic group, and sibling status in a population. In a cross-sectional observation study, a total of 240 study participants were recruited via clustered sampling technique. Demographic information was collected using a questionnaire. Fingerprints were collected using fingerprint ink pad and classified according to Henry’s classification system. The association of fingerprint patterns with fingers, gender, ethnic groups and relationship-status was determined using the chi-square test. Statistical significance was set at P≤0.05. The frequency of arch, composite, loop, and whorl patterns were comparable across the five fingers as well as between gender and ethnic group for both the right and left arms (P>0.05). However, there was significant variation in the frequency of arch, composite, loop, and whorl fingerprint patterns for all the fingers between siblings and non-siblings (P<0.05). The ulnar loop was found to be the most occurring fingerprint sub-pattern across the five fingers, between the gender and ethnic group as well as between sibling and non-siblings. Plain whorl, radial loop, plain arch, tented arch, central pocket whorl, double whorl, loop arch composite and whorl arch composite followed respectively. There is similarity in fingerprint patterns across the fingers as well as between gender and ethnic groups but not siblings and non-siblings in Western Kenya. Therefore, fingerprint patterns may not be used to discriminate fingers, gender, and ethnic groups but can be used as a tool to distinguish between siblings and non-siblings in the population 2023-12-01T00:00:00Z