A Direct from Blood/Plasma Reverse Transcription–Polymerase Chain Reaction for Dengue Virus Detection in Point-of-Care Settings
Bibliographic record
Abstract
Infection with dengue virus (DENV) is widespread across tropical regions and can result in severe disease. Early diagnosis is important both for patient management and to differentiate infections that present with similar symptoms, such as malaria, chikungunya, and Zika. Rapid diagnostic tests that are used presently for point-of-care detection of DENV antigens lack the sensitivity of molecular diagnostics that detect viral RNA. However, no molecular diagnostic test for DENV is available for use in field settings. In this study, we developed and validated a reverse transcription–polymerase chain reaction (RT-PCR) for the detection of DENV adapted for use in field settings. Reverse transcription–polymerase chain reaction was performed directly from plasma samples without RNA extraction. The assay detected all four serotypes of DENV spiked into blood or plasma. Our RT-PCR does not cross-react with pathogens that cause symptoms that overlap with dengue infection. The test performed equally well in a conventional laboratory qPCR instrument and a small, low-cost portable instrument that can be used in a field setting. The lower limit of detection for the assay was 1 × 104 genome copy equivalents/mL in blood. Finally, we validated our test using 126 archived patient samples. The sensitivity of our RT-PCR was 76.7% (95% CI: 65.8–87.9%) on the conventional instrument, and 78.3% (95% CI: 65.8–87.9%) on the field instrument, when compared with the RealStar Dengue RT-PCR Kit 2.0. The molecular test described here is user-friendly, low-cost, and can be used in regions with limited laboratory capabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".