SynthesisThe Journal of infectious diseases2024
Host Transcriptional Meta-signatures Reveal Diagnostic Biomarkers for Plasmodium falciparum Malaria.
Synthesis in The Journal of infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- C1QB-Mediated Immunopathology in a Murine Malaria Model: A Multi-Omics Validation for Diagnostic and Therapeutic Targeting.International journal of molecular sciences · 2026Article
- Methylene blue treatment of fatal cerebral malaria and identification of potential blood biomarkers.Nature communications · 2025Article
- Systems Biological Analysis of Immune-Metabolic Host Responses to Distinct Malaria Parasites.ACS omega · 2025Article
- How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms.Malaria journal · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTranscriptomics has been used to evaluate immune responses during malaria in diverse cohorts worldwide. However, the high heterogeneity of cohorts and poor generalization of transcriptional signatures reported in each study limit their potential clinical applications.
methodsWe compiled 28 public data sets containing 1556 whole-blood or peripheral blood mononuclear cell transcriptome samples. We estimated effect sizes with Hedge's g value and the DerSimonian-Laird random-effects model for meta-analyses of uncomplicated malaria. Random forest models identified gene signatures that discriminate malaria from bacterial infections or malaria severity. Parasitological, hematological, immunological, and metabolomics data were used for validation.
resultsWe identified 3 gene signatures: the uncomplicated Malaria Meta-Signature, which discriminates Plasmodium falciparum malaria from uninfected controls; the Malaria or Bacteria Signature, which distinguishes malaria from sepsis and enteric fever; and the cerebral Malaria Meta-Signature, which characterizes individuals with cerebral malaria. These signatures correlate with clinical hallmark features of malaria. Blood transcription modules indicate immune regulation by glucocorticoids, whereas cell development and adhesion are associated with cerebral malaria.
conclusionsTranscriptional meta-signatures reflecting immune cell responses provide potential biomarkers for translational innovation and suggest critical roles for metabolic regulators of inflammation during malaria.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.