A Loop-Mediated Isothermal Amplification Assay for Rapid Detection of Pectobacterium aroidearum that Causes Soft Rot in Konjac
Bibliographic record
Abstract
Bacterial soft rot caused by Pectobacterium species is a serious disease in konjac (Amorphophallus konjac), a healthy source of starch particularly in East Asia. An effective diagnostic method is crucial to control the disease and reduce losses in konjac production. In this study, we evaluated a loop-mediated isothermal amplification (LAMP) assay with a specific primer set for the rapid and accurate detection of P. aroidearum. A comparative genomics approach was used to identify the specific genes suitable for the design of LAMP primers. The candidate target genes were determined through a first-round comparison with a 50-genome nucleotide database, and subjected to a second-round screening with the GenBank NR database. As a result, nine specific genes of P. aroidearum were selected for LAMP primer design. After screening of the primers, the primer set 1675-1 was chosen for LAMP detection owing to its high specificity and sensitivity. The LAMP assay could detect the presence of P. aroidearum genomic DNA at a concentration as low as 50 fg and 1.2 × 104 CFU/g artificially infected soil within 40 min at 65 °C. Subsequently, this primer set was successfully used to specifically detect P. aroidearum in naturally infected and non-symptomatic plant samples or soil samples from the field. This study indicates that a comparative genomic approach may facilitate the development of highly specific primers for LAMP assays, and a LAMP diagnostic assay with the specific primer set 1675-1 should contribute to the rapid and accurate detection of soft-rot disease in konjac at an early stage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".