Evaluating Myxovirus Resistance Protein A-Based Rapid Testing Combined with Pathogen Sequencing for Arboviral and Incidental Viral Infection Surveillance in Senegal
Bibliographic record
Abstract
Accurate differentiation between viral and bacterial infections remains challenging in resource-limited, arbovirus-endemic settings, leading to antibiotic misuse and diagnostic uncertainty. Myxovirus resistance protein A (MxA), an interferon-induced host biomarker, may offer a pathogen-agnostic approach to improve rapid diagnosis and clinical triage. We evaluated the performance of an MxA rapid diagnostic test (RDT) using archived samples from febrile patients collected during dengue virus (DENV) and chikungunya virus (CHIKV) outbreaks in Senegal. We tested 171 blood samples from patients with acute febrile illness using an MxA RDT and RT-qPCR for DENV and CHIKV. Samples with discordant results (MxA-positive and RT-qPCR-negative) underwent metagenomic and hybrid-capture Illumina-based sequencing to detect missed infections. Sequencing data were analyzed using maximum-likelihood phylogenetics to assess viral lineage placement. The MxA RDT demonstrated moderate-to-high sensitivity (70.0%-85.1%, depending on virus) and moderate specificity (70.2%) for detecting primary arboviral infections. Among discordant samples, sequencing revealed previously missed pathogens, including DENV serotype 3 (genotype III), Parvovirus B19 (B19V), and Torque teno virus (TTV). Detection of B19V and TTV highlights the broader clinical utility of host-response biomarkers to uncover unexpected viral pathogens in high-diversity settings. MxA's longer persistence than viral RNA enables detection of recent infections missed by PCR. Combined with sequencing, this broadens the diagnostic window, improves clinical triage, and supports identification of underdiagnosed viruses. Future research should integrate MxA testing into routine clinical care and surveillance protocols to enhance outbreak responses in resource-limited regions.IMPORTANCETimely and equitable viral diagnosis is vital in outbreak-prone regions where advanced laboratories are scarce. This study shows how a simple, rapid test for the host biomarker myxovirus resistance protein A can provide real-time detection of viral infections such as dengue and chikungunya, even in remote or frontline health centers. When paired with pathogen sequencing, the test also uncovers infections that standard PCR may miss. This integrated approach demonstrates how field-deployable diagnostics can operate both during and between epidemics, strengthening outbreak preparedness, improving patient triage, and advancing laboratory equity worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".