According to the WHO, almost two thirds of the Ethiopian population are at risk of contracting malaria, where infection with Plasmodium falciparum accounts for approximately 60% of cases today. The risk of artemisinin resistance spreading from SE Asia to Africa is a major concern. We conducted a 28-day in vivo efficacy trial of Artemether-Lumefantrine (Co-Artem) for treatment of uncomplicated malaria (n = 97) in the Gondar Region, North West Ethiopia in 2017–2018. Our results confirmed 100% adequate clinical and parasitological response (ACPR) with no parasites observed at day 3 by microscopy. Further analysis of day 0 samples showed the expansion of a kelch13 mutation R622I to 9.5% from 2.4% of isolates reported three years earlier. Closer examination of the R622I mutation in vitro is warranted.
Atovaquone-proguanil (AP) is used as treatment for uncomplicated malaria, and as a chemoprophylactic agent against Plasmodium falciparum . Imported malaria remains one of the top causes of fever in Canadian returning travelers. Twelve sequential whole-blood samples before and after AP treatment failure were obtained from a patient diagnosed with P. falciparum malaria upon their return from Uganda and Sudan. Ultradeep sequencing was performed on the cytb, dhfr, and dhps markers of treatment resistance before and during the episode of recrudescence. Haplotyping profiles were generated using three different approaches: msp2-3D7 agarose and capillary electrophoresis, and cpmp using amplicon deep sequencing (ADS). A complexity of infection (COI) analysis was conducted. De novo cytb Y268C mutants strains were observed during an episode of recrudescence 17 days and 16 h after the initial malaria diagnosis and AP treatment initiation. No Y268C mutant reads were observed in any of the samples prior to the recrudescence. SNPs in the dhfr and dhps genes were observed upon initial presentation. The haplotyping profiles suggest multiple clones mutating under AP selection pressure (COI > 3). Significant differences in COI were observed by capillary electrophoresis and ADS compared to the agarose gel results. ADS using cpmp revealed the lowest haplotype variation across the longitudinal analysis. Our findings highlight the value of ultra-deep sequencing methods in the understanding of P. falciparum haplotype infection dynamics. Longitudinal samples should be analyzed in genotyping studies to increase the analytical sensitivity.
Context.— Clostridioides difficile infection (CDI) is the world-leading cause of infectious nosocomial diarrhea and pseudomembranous colitis. Antibiotics are the first line of treatment against CDI despite the high likelihood of antibiotic failure and/or recurrence. More data are needed to correlate clinical variables with 16S rRNA microbiome profiles in CDI-infected patients. Objective.— To determine the relationship(s) between a patient's clinical factors and the stool bacteriome of CDI-positive patients and CDI-negative patients with diarrheal symptoms. Design.— This study used stool samples and clinical data from 358 patients with nosocomial diarrhea, who were divided by their CDI diagnosis (CDI-negative: n = 180; CDI-positive; n = 178). The stool bacteriome was profiled by amplicon deep sequencing of the 16S rRNA gene, followed by correlating clinical data. Results.— The stool bacteriome was significantly different by severity assessment regardless of CDI status. Phyla and species varied significantly by CDI diagnosis. Severity, defined as a serum white blood cell count greater than 15 cells/μL and/or a creatinine level greater than 1.5 mg/dL, correlated significantly with dysbiosis of the stool bacteriome profile of CDI-positive patients compared to CDI-negative patients. Serum white blood cell count was significantly higher in patients with bacterial dysbiosis, and high levels of creatinine were associated with low bacteriome diversity. Conclusions.— Clinical severity of CDI influences the stool microbiome of infected patients. To date, this study has the largest data set comparing 16S rRNA microbiome profiles and clinical variables between CDI-infected and noninfected individuals.
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