Browsing by Author "Ateko, R.O."
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Item Apolipoprotein E genetic variation, atherogenic index and cardiovascular disease risk assessment in an African population: An analysis of HIV and malaria patients in Ghana(PLOS ONE, 2023) Thomford, N.E.; Anyanful, A.; Ateko, R.O.; et al.Background Apolipoprotein E is involved in lipid transport and clearance of lipoprotein through low-density lipoprotein receptors (LDLR). ApoE variation has been linked to cardiovascular disease (CVD) risk. There are 3 isoforms of ApoE which originate from two non-synonymous single nucleotide polymorphisms denoted as ε2, ε3 and ε4. The ε2 isoform is implicated in higher levels of atherogenic lipoprotein with the ε4 isoform causing LDLR downregulation. This leads to variable effects and differential CVD risk. Malaria and HIV are life-threatening diseases affecting several countries globally especially in sub-Saharan Africa. Parasite and viral activities have been implicated in lipid dysregulation leading to dyslipidaemia. This study examined ApoE variation and CVD risk assessment in malaria and HIV patients. Methods We compared 76 malaria-only, 33 malaria-HIV coinfected, 21-HIV-only and 31 controls from a tertiary health facility in Ghana. Fasting venous blood samples were taken for ApoE genotyping and lipid measurements. Clinical and laboratory data were collected with ApoE genotyping performed using Iplex Gold microarray and PCR-RFLP. Cardiovascular disease risk was calculated using the Framingham BMI and cholesterol risk and Qrisk3 tools. Results The frequency of C/C genotype for rs429358 was 9.32%, whiles T/T genotype for rs7412 was found in 2.48% of all participants. ε3/ε3 was the most distributed ApoE genotype accounting for 51.55% of the total participants whiles ε2/ε2 was found in 2.48% of partici pants, with 1 in malaria-only and 3 in HIV-only patients. There was a significant association between ε4+ and high TG (OR = 0.20, CI; 0.05–0.73; p = 0.015), whiles ε2+ was significantly associated with higher BMI (OR; 0.24, CI; 0.06–0.87; p = 0.030) and higher Castelli Risk Index II in females (OR = 11.26, CI; 1.37–92.30; p = 0.024). A higher proportion of malaria only participants had a moderate to high 10-year CVD risk. Conclusion Overall malaria patients seem to have a higher CVD risk though the means through which this occurs may be poorly understood. ε2/ε2 genotypes was observed in our population at a lower frequency. Further studies are vital to determine CVD risk in malaria and how this occurs.Item Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology(OMICS A Journal of Integrative Biology, 2020-05-07) Ateko, R.O.; Dzobo, K.; Agamah, F.E.; Bope, C.D.; Thomford, N.E; Chimusa, E.; Mazandu, G.K.; Ntumba, S.B.; Dandara, C.; Wonkam, A.Artificial intelligence (AI) is one of the key drivers of digital health. Digital health and AI applications in medicine and biology are emerging worldwide, not only in resource-rich but also resource-limited regions. AI predates to the mid-20th century, but the current wave of AI builds in part on machine learning (ML), big data, and algorithms that can learn from massive amounts of online user data from patients or healthy persons. There are lessons to be learned from AI applications in different medical specialties and across developed and resourcelimited contexts. A case in point is congenital heart defects (CHDs) that continue to plague sub-Saharan Africa, which calls for innovative approaches to improve risk prediction and performance of the available diagnostics. Beyond CHDs, AI in cardiology is a promising context as well. The current suite of digital health applications in CHD and cardiology include complementary technologies such as neural networks, ML, natural language processing and deep learning, not to mention embedded digital sensors. Algorithms that build on these advances are beginning to complement traditional medical expertise while inviting us to redefine the concepts and definitions of expertise in molecular diagnostics and precision medicine. We examine and share here the lessons learned in current attempts to implement AI and digital health in CHD for precision risk prediction and diagnosis in resource-limited settings. These top 10 lessons on AI and digital health summarized in this expert review are relevant broadly beyond CHD in cardiology and medical innovations. As with AI itself that calls for systems approaches to data capture, analysis, and interpretation, both developed and developing countries can usefully learn from their respective experiences as digital health continues to evolve worldwide.