Validation of a Preformulated, Field Deployable, Recombinase Polymerase Amplification Assay for Phytophthora Species
Bibliographic record
Abstract
Recombinase polymerase amplification (RPA) assays are valuable molecular diagnostic tools that can detect and identify plant pathogens in the field without time-consuming DNA extractions. Historically, RPA assay reagents were commercially available as a lyophilized pellet in microfuge strip tubes, but have become available in liquid form more recently—both require the addition of primers and probes prior to use, which can be challenging to handle in a field setting. Lyophilization of primers and probes, along with RPA reagents, contained within a single tube limits the risk of contamination, eliminates the need for refrigeration, as the lyophilized reagents are stable at ambient temperatures, and simplifies field use of the assays. This study investigates the potential effect of preformulation on assay performance using a previously validated Phytophthora genus-specific RPA assay, lyophilized with primers and probes included with the RPA reagents. The preformulated lyophilized Phytophthora RPA assay was compared with a quantitative polymerase chain reaction (qPCR) assay and commercially available RPA kits using three qPCR platforms (BioRad CFX96, QuantStudio 6 and Applied Biosystems ViiA7) and one isothermal platform (Axxin T16-ISO RPA), with experiments run in four separate labs. The assay was tested for sensitivity (ranging from 500 to 0.33 pg of DNA) and specificity using purified oomycete DNA, as well as crude extracts of Phytophthora-infected and non-infected plants. The limit of detection (LOD) using purified DNA was 33 pg in the CFX96 and ViiA7 qPCR platforms using the preformulated kits, while the Axxin T16-ISO RPA chamber and the QuantStudio 6 platform could detect down to 3.3 pg with or without added plant extract. The LOD using a crude plant extract for the BioRad CFX96 was 330 pg, whereas the LOD for the ViiA7 system was 33 pg. These trials demonstrate the consistency and uniformity of pathogen detection with preformulated RPA kits for Phytophthora detection when conducted by different labs using different instruments for measuring results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".