Development and validation of a one-step reverse transcription loop-mediated isothermal amplification (RT-LAMP) for rapid detection of ZIKV in patient samples from Brazil
Bibliographic record
Abstract
We have previously developed and validated a one-step assay based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) for rapid detection of the Zika virus (ZIKV) from mosquito samples. Patient diagnosis of ZIKV is currently carried out in centralized laboratories using the reverse transcription-quantitative polymerase chain reaction (RT-qPCR), which, while the gold standard molecular method, has several drawbacks for use in remote and low-resource settings, such as high cost and the need of specialized equipment. Point-of-care (POC) diagnostic platforms have the potential to overcome these limitations, especially in low-resource countries where ZIKV is endemic. With this in mind, here we optimized and validated our RT-LAMP assay for rapid detection of ZIKV from patient samples. We found that the assay detected ZIKV from diverse sample types (serum, urine, saliva, and semen) in as little as 20 min, without RNA extraction. The RT-LAMP assay was highly specific and up to 100 times more sensitive than RT-qPCR. We then validated the assay using 100 patient serum samples collected from suspected cases of arbovirus infection in the state of Pernambuco, which was at the epicenter of the last Zika epidemic. Analysis of the results, in comparison to RT-qPCR, found that the ZIKV RT-LAMP assay provided sensitivity of 100%, specificity of 93.75%, and an overall accuracy of 95.00%. Taken together, the RT-LAMP assay provides a straightforward and inexpensive alternative for the diagnosis of ZIKV from patients and has the potential to increase diagnostic capacity in ZIKV-affected areas, 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".