Genetic diversity and genotype multiplicity of Plasmodium falciparum infection in patients with uncomplicated malaria in Chewaka district, Ethiopia.

BACKGROUND: Genetic diversity in Plasmodium falciparum poses a major threat to malaria control and elimination interventions. Characterization of the genetic diversity of P. falciparum strains can be used to assess intensity of parasite transmission and identify potential deficiencies in malaria control programmes, which provides vital information to evaluating malaria elimination efforts. This study investigated the P. falciparum genetic diversity and genotype multiplicity of infection in parasite isolates from cases with uncomplicated P. falciparum malaria in Southwest Ethiopia. METHODS: A total of 80 P. falciparum microscopy and qPCR positive blood samples were collected from study participants aged 6 months to 60 years, who visited the health facilities during study evaluating the efficacy of artemether-lumefantrine from September-December, 2017. Polymorphic regions of the msp-1 and msp-2 were genotyped by nested polymerase chain reactions (nPCR) followed by gel electrophoresis for fragment analysis. RESULTS: Of 80 qPCR-positive samples analysed for polymorphisms on msp-1 and msp-2 genes, the efficiency of msp-1 and msp-2 gene amplification reactions with family-specific primers were 95% and 98.8%, respectively. Allelic variation of 90% (72/80) for msp-1 and 86.2% (69/80) for msp-2 were observed. K1 was the predominant msp-1 allelic family detected in 20.8% (15/72) of the samples followed by MAD20 and RO33. Within msp-2, allelic family FC27 showed a higher frequency (26.1%) compared to IC/3D7 (15.9%). Ten different alleles were observed in msp-1 with 6 alleles for K1, 3 alleles for MAD20 and 1 allele for RO33. In msp-2, 19 individual alleles were detected with 10 alleles for FC27 and 9 alleles for 3D7. Eighty percent (80%) of isolates had multiple genotypes and the overall mean multiplicity of infection was 3.2 (95% CI 2.87-3.46). The heterozygosity indices were 0.43 and 0.85 for msp-1 and msp-2, respectively. There was no significant association between multiplicity of infection and age or parasite density. CONCLUSIONS: The study revealed high levels of genetic diversity and mixed-strain infections of P. falciparum populations in Chewaka district, Ethiopia, suggesting that both endemicity level and malaria transmission remain high and that strengthened control efforts are needed in Ethiopia.

Therapeutic efficacy of artemether-lumefantrine in the treatment of uncomplicated Plasmodium falciparum malaria in Chewaka District, Ethiopia.

BACKGROUND: The efficacy of artemether-lumefantrine (AL) for treatment of uncomplicated Plasmodium falciparum malaria in south-western Ethiopia is poorly documented. Regular monitoring of drug efficacy is an important tool for supporting national treatment policies and practice. This study investigated the therapeutic efficacy of AL for the treatment of P. falciparum malaria in Ethiopia. METHODS: The study was a one-arm, prospective, evaluation of the clinical and parasitological, responses to directly observed treatment with AL among participants 6 months and older with uncomplicated P. falciparum malaria. Real-time polymerase chain reaction (PCR) and nested PCR reaction methods were used to quantify and genotype P. falciparum. A modified protocol based on the World Health Organization 2009 recommendations for the surveillance of anti-malarial drug efficacy was used for the study with primary outcomes, clinical and parasitological cure rates at day-28. Secondary outcomes assessed included patterns of fever and parasite clearance. Cure rate on day-28 was assessed by intention to treat (ITT) and per protocol (PP) analysis. Parasite genotyping was also performed at baseline and at the time of recurrence of parasitaemia to differentiate between recrudescence and new infection. RESULTS: Of the 80 study participants enrolled, 75 completed the follow-up at day-28 with ACPR. For per protocol (PP) analysis, PCR-uncorrected and-corrected cure rate of AL among the study participants was 94.7% (95% CI 87.1-98.5) and 96% (95% CI 88.8-99.2), respectively. For intention to treat (ITT) analysis, the cure rate was 90% (95% CI 88.8-99.2). Based on Kaplan-Meier survival estimate, the cumulative incidence of failure rate of AL was 3.8% (95% CI 1.3-11.4). Only three participants 3.8% (95% CI 0.8-10.6) of the 80 enrolled participants were found to be positive on day-3. The day three-positive participants were followed up to day 28 and did not correspond to treatment failures observed during follow-up. Only 7.5% (6/80) of the participants were gametocyte-positive on enrollment and gametocytaemia was absent on day-2 following treatment with AL. CONCLUSIONS: The therapeutic efficacy of AL is considerably high (above 90%). AL remained highly efficacious in the treatment of uncomplicated malaria in the study area resulted in rapid fever and parasite clearance as well as low gametocyte carriage rates despite the use of this combination for more than 15 years.

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM).

Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.