A Loop-Mediated Isothermal Amplification Assay for Rapid Detection of Pectobacterium aroidearum that Causes Soft Rot in Konjac
Why this work is in the frame
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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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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 | 0.001 | 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 it