Phylogeographic analysis of dengue virus serotype 1 and cosmopolitan serotype 2 in Africa.

OBJECTIVES: The origin and spread of dengue virus (DENV) circulating in Africa remain poorly characterized, with African sequences representing <1% of global sequence data. METHODS: Whole genome sequencing was performed on serum samples (n = 29) from an undifferentiated fever study in 2016 in the Democratic Republic of Congo (DRC), and from febrile travelers returning from Africa. The evolutionary history of the newly acquired African DENV-1 (n = 1) and cosmopolitan genotype DENV-2 (n = 18) genomes was reconstructed using a phylogeographic, time-scaled Bayesian analysis on a curated DENV panel including all known African sequences. RESULTS: A minimum of 10 and eight introductions could be identified into Africa for DENV-1 and cosmopolitan DENV-2, respectively, almost all originating from Asia. Three introductions were previously unknown. The currently circulating virus comprises mainly the recently introduced clades and one long-established African clade. Robust geographical clustering suggests limited spread of DENV after each introduction. Our data identified the DRC as the source of the 2018 Angolan DENV-2 epidemic, and similarly, the 2013 Angolan DENV-1 outbreak as the origin of our DRC study. CONCLUSION: Active genomic surveillance of DENV in Africa at the portals of entry might help early outbreak response and limit sero- and genotype spread and human disease burden.

Dengue and chikungunya among outpatients with acute undifferentiated fever in Kinshasa, Democratic Republic of Congo: A cross-sectional study.

BACKGROUND: Pathogens causing acute fever, with the exception of malaria, remain largely unidentified in sub-Saharan Africa, given the local unavailability of diagnostic tests and the broad differential diagnosis. METHODOLOGY: We conducted a cross-sectional study including outpatient acute undifferentiated fever in both children and adults, between November 2015 and June 2016 in Kinshasa, Democratic Republic of Congo. Serological and molecular diagnostic tests for selected arboviral infections were performed on blood, including PCR, NS1-RDT, ELISA and IFA for acute, and ELISA and IFA for past infections. RESULTS: Investigation among 342 patients, aged 2 to 68 years (mean age of 21 years), with acute undifferentiated fever (having no clear focus of infection) revealed 19 (8.1%) acute dengue-caused by DENV-1 and/or DENV-2 -and 2 (0.9%) acute chikungunya infections. Furthermore, 30.2% and 26.4% of participants had been infected in the past with dengue and chikungunya, respectively. We found no evidence of acute Zika nor yellow fever virus infections. 45.3% of patients tested positive on malaria Rapid Diagnostic Test, 87.7% received antimalarial treatment and 64.3% received antibacterial treatment. DISCUSSION: Chikungunya outbreaks have been reported in the study area in the past, so the high seroprevalence is not surprising. However, scarce evidence exists on dengue transmission in Kinshasa and based on our data, circulation is more important than previously reported. Furthermore, our study shows that the prescription of antibiotics, both antibacterial and antimalarial drugs, is rampant. Studies like this one, elucidating the causes of acute fever, may lead to a more considerate and rigorous use of antibiotics. This will not only stem the ever-increasing problem of antimicrobial resistance, but will-ultimately and hopefully-improve the clinical care of outpatients in low-resource settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT02656862.

Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data.

Newborn screening programs for severe metabolic disorders using tandem mass spectrometry are widely used. Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) is the most prevalent mitochondrial fatty acid oxidation defect (1:15,000 newborns) and it has been proven that early detection of this metabolic disease decreases mortality and improves the outcome. In previous studies, data mining methods on derivatized tandem MS datasets have shown high classification accuracies. However, no machine learning methods currently have been applied to datasets based on non-derivatized screening methods. A dataset with 44,159 blood samples was collected using a non-derivatized screening method as part of a systematic newborn screening by the PCMA screening center (Belgium). Twelve MCADD cases were present in this partially MCADD-enriched dataset. We extended three data mining methods, namely C4.5 decision trees, logistic regression and ridge logistic regression, with a parameter and threshold optimization method and evaluated their applicability as a diagnostic support tool. Within a stratified cross-validation setting, a grid search was performed for each model for a wide range of model parameters, included variables and classification thresholds. The best performing model used ridge logistic regression and achieved a sensitivity of 100%, a specificity of 99.987% and a positive predictive value of 32% (recalibrated for a real population), obtained in a stratified cross-validation setting. These results were further validated on an independent test set. Using a method that combines ridge logistic regression with variable selection and threshold optimization, a significantly improved performance was achieved compared to the current state-of-the-art for derivatized data, while retaining more interpretability and requiring less variables. The results indicate the potential value of data mining methods as a diagnostic support tool.