Development of a Multiplex PCR for the Specific Detection of Phytoplasma Subgroups 16SrIX-B and 16SrIX-C
Bibliographic record
Abstract
During the early 1990s, almond witches’ broom (AlmWB), a disease affecting stone fruits, was reported in Lebanon and Iran. The disease quickly escalated into an epidemic that destroyed hundreds of thousands of almonds, peaches, and nectarine trees due to its invasive and destructive nature. The causal agent was eventually identified to be ‘ Candidatus Phytoplasma phoenicium’, a phytoplasma belonging to subgroup 16SrIX-B. There are currently no effective curative treatments, making the eradication of infected trees the only remaining option. Therefore, stringent quarantine measures and potent detection tools are imperative for early detection and effective prevention of AlmWB phytoplasma. Although there are currently no commercially available phytoplasma detection serological tests, there are several PCR-based assays for the detection of ‘ Ca. P. phoenicium’ and other phytoplasmas. Consequently, the development of accurate and sensitive assays is needed. In this study, we designed primers for specific, sensitive, and simultaneous detection of 16SrIX-B (AlmWB) and 16SrIX-C (Picris echioides yellows) phytoplasmas without the need for restriction fragment length polymorphism. The PCR assays were highly efficient, specific, and reproducible for the detection of the two phytoplasmas in multiplex PCR. The multiplex assay detected AlmWB and Picris echioides yellows targets in mixed infections up to a sensitivity of 100 pg/µl of total plant DNA. Our developed multiplex PCR assay is suitable for quarantine screening of woody tree germplasm and for surveying aimed at discriminating 16SrIX-B, a quarantine pest, from the closely related 16SrIX-C, a non-quarantine phytoplasma, with the aim of preventing or mitigating AlmWB.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".