ArticleThe Journal of infectious diseases2017
Transcriptional Categorization of the Etiology of Pneumonia Syndrome in Pediatric Patients in Malaria-Endemic Areas.
Article in The Journal of infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- RNA-seq research landscape in Africa: systematic review reveals disparities and opportunities.European journal of medical research · 2023Pooled it
- Transcript host-RNA signatures to discriminate bacterial and viral infections in febrile children.Pediatric research · 2022Pooled it
- A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia.iScience · 2025Article
- Predictive signature of murine and human host response to typical and atypical pneumonia.BMJ open respiratory research · 2024Article
- Prognostic accuracy of biomarkers of immune and endothelial activation in Mozambican children hospitalized with pneumonia.PLOS global public health · 2023Article
- Machine learning predictive modelling for identification of predictors of acute respiratory infection and diarrhoea in Uganda's rural and urban settings.PLOS global public health · 2022Article
- Biomarkers to Distinguish Bacterial From Viral Pediatric Clinical Pneumonia in a Malaria-Endemic Setting.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021Article
- USP10 alleviates sepsis-induced acute kidney injury by regulating Sirt6-mediated Nrf2/ARE signaling pathway.Journal of inflammation (London, England) · 2021Article
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Authors and funding
17 authors.
Funding
Abstract
Background: Pediatric acute respiratory distress in tropical settings is very common. Bacterial pneumonia is a major contributor to morbidity and mortality rates and requires adequate diagnosis for correct treatment. A rapid test that could identify bacterial (vs other) infections would have great clinical utility. Methods and Results: We performed RNA (RNA-seq) sequencing and analyzed the transcriptomes of 68 pediatric patients with well-characterized clinical phenotype to identify transcriptional features associated with each disease class. We refined the features to predictive models (support vector machine, elastic net) and validated those models in an independent test set of 37 patients (80%-85% accuracy). Conclusions: We have identified sets of genes that are differentially expressed in pediatric patients with pneumonia syndrome attributable to different infections and requiring different therapeutic interventions. Findings of this study demonstrate that human transcription signatures in infected patients recapitulate the underlying biology and provide models for predicting a bacterial diagnosis to inform treatment.
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