Development of a reverse transcription loop-mediated isothermal amplification assay for the rapid detection of <i>Pepper mottle virus</i>
Bibliographic record
Abstract
Pepper mottle virus (PepMoV) is a widespread threat to vegetable crop production in the USA and south-east Asia. We describe the development of a reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay to detect PepMoV. The RT-LAMP assay was based on a set of four primers that match a specific region of the coat protein gene in the PepMoV genome. The detection limit of conventional RT-PCR detection was 1.47 × 10−4 µg µL−1 of cDNA, whereas RT-LAMP was 10 times more sensitive. Using RT-LAMP, PepMoV detection was also highly specific, showing no cross-activity with four other potyviruses. Sixty-nine field samples collected from symptomatic pepper plants growing in five South China provinces were tested for the presence of PepMoV infection by performing an RT-LAMP assay as well as a conventional RT-PCR. Both methods detected PepMoV in 18 samples, demonstrating that the PepMoV-specific RT-LAMP assay could be a useful alternative tool for the diagnosis and epidemiological surveillance of PepMoV infections. The RT-LAMP assay also has the advantages that it can be performed in a low-tech environment and is quicker and cheaper to perform than conventional RT-PCR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".