Development of a Loop-Mediated Isothermal Amplification Method for the Rapid Detection of Phytopythium vexans
Bibliographic record
Abstract
Brown root rot caused by Phytopythium vexans is a new destructive root disease on many plants such as Gingko, Citrus, kiwifruit, and ramie. The establishment of loop-mediated isothermal amplification (LAMP) technology for detecting P. vexans can help monitor and control brown root rot quickly, efficiently, and accurately. LAMP technology is known for its simplicity, sensitivity, and speed; and it does not require any specialized equipment – a water bath or a thermoblock is sufficient for isothermal amplifications. LAMP products can be visualized by using hydroxy naphthol blue (HNB) dye or agarose gel electrophoresis. In this study, by searching and comparing the internal transcribed spacer (ITS) sequences of P. vexans and the related species in oomycete genera Pythium, Phytopythium , and Phytophthora , we designed specific primers targeting the ITS gene region of P. vexans . Using HNB dye, we established a LAMP technique for rapid detection of P. vexans by visible color change. In addition, we optimized the protocol to enhance both sensitivity and specificity for P. vexans detection. Under the optimized condition, our protocol based on LAMP technology could detect as low as 24 copies of the P. vexans genomic DNA, which is ∼100 times more sensitive than conventional PCR. This method can successfully detect P. vexans using cell suspensions from P. vexans – infected ramie root tissues.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".