Development and field validation of a reverse transcription loop-mediated isothermal amplification assay (RT-LAMP) for the rapid detection of chikungunya virus in patient and mosquito samples
Bibliographic record
Abstract
OBJECTIVES: We aimed to develop a reverse transcription loop-mediated isothermal amplification (RT-LAMP) platform for the rapid detection of chikungunya virus (CHIKV) in both patient and mosquito samples from Brazil. METHODS: We optimized an RT-LAMP assay and then evaluated the specificity and sensitivity using visual detection. In comparison with the RT-qPCR reference method, we validated the utility of this assay as a molecular diagnostic test in a reference laboratory for arbovirus diagnostics using 100 serum samples collected from suspected CHIKV cases. RESULTS: Our RT-LAMP assay specifically detected CHIKV without cross-reactivity against other arboviruses. The limit of detection of our RT-LAMP was estimated in -1.18 PFU (confidence interval [CI] ranging from -2.08 to 0.45), resulting in a similar analytical sensitivity when directly compared with the reference standard RT-qPCR assay. Then, we demonstrate the ability of our RT-LAMP assay to detect the virus in different human specimens (serum, urine, and saliva), and crude lysate of Aedes aegypti mosquitoes in as little as 20-30 minutes and without a separate RNA isolation step. Lastly, we showed that our RT-LAMP assay could be lyophilized and reactivated by adding water, indicating potential for room-temperature storage. Our RT-LAMP had a clinical sensitivity of 100% (95% CI, 90.97-100.00%), clinical specificity of 96.72% (95% CI, 88.65-99.60%), and overall accuracy of 98.00% (95% CI, 92.96-99.76%). DISCUSSION: Taken together, these findings indicate that the RT-LAMP assay reported here solves important practical drawbacks to the deployment of molecular diagnostics in the field and can be used to improve testing capacity, particularly in low- and middle-income countries.
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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 | 0.000 | 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".