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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".