Loop-Mediated Isothermal Amplification (LAMP) for the Diagnosis of Zika Virus: A Review
Bibliographic record
Abstract
The recent outbreak of Zika virus (ZIKV) in the Americas and its devastating developmental and neurological manifestations has prompted the development of field-based diagnostics that are rapid, reliable, handheld, specific, sensitive, and inexpensive. The gold standard molecular method for lab-based diagnosis of ZIKV, from either patient samples or insect vectors, is reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The method, however, is costly and requires lab-based equipment and expertise, which severely limits its use as a point-of-care (POC) tool in resource-poor settings. Moreover, given the lack of antivirals or approved vaccines for ZIKV infection, a POC diagnostic test is urgently needed for the early detection of new outbreaks and to adequately manage patients. Loop-mediated isothermal amplification (LAMP) is a compelling alternative to RT-qPCR for ZIKV and other arboviruses. This low-cost molecular system can be freeze-dried for distribution and exhibits high specificity, sensitivity, and efficiency. A growing body of evidence suggests that LAMP assays can provide greater accessibility to much-needed diagnostics for ZIKV infections, especially in developing countries where the ZIKV is now endemic. This review summarizes the different LAMP methods that have been developed for the virus and summarizes their features, advantages, and limitations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".