Comparison of RNA extraction methods for RT-PCR detection of <i>Coconut cadang-cadang viroid</i> variant in orange spotting oil palm leaves
Bibliographic record
Abstract
Coconut cadang-cadang viroid (CCCVd) is a lethal disease that has devastated the coconut industry in the Philippines. Recently, a CCCVd variant associated with orange spotting of foliage, which is a phenomenon reported in the Malaysian oil palm industry, occurring as an isolated palm with no similar symptoms on the adjacent neighbouring palms. The presence of this viroid in oil palms is difficult to detect as it is found in very low concentrations. Thus, an efficient RNA extraction method for isolating the CCCVd variant and to detect it through reverse transcriptase polymerase chain reaction (RT-PCR) using specific primers for CCCVd was required. Leaf samples from symptomatic and asymptomatic oil palms were collected from various locations in Malaysia, namely, Negeri Sembilan, Selangor and Perak. The samples were subjected to three RNA extraction methods – natrium chloride EDTA Tris-HCl mercaptoethanol extraction (NETME), polyethylene glycol extraction (PEG) and cetyltrimethylammonium bromide extraction (CTAB). The quantification of the RNA, based on optical density (OD) and concentration, showed that the CTAB extraction was the best, followed by the NETME extraction. The RNA extraction using PEG resulted in poor yields and had the lowest purity. The viroid was detected in all of the samples extracted via CTAB and NETME by RT-PCR with reduced starting material compared with PEG extracted RNA. A BLAST analysis indicated that the viroid sequences are highly conserved and shared a 93% sequence identity with the CCCVd246 variant from oil palm. Phylogenetic analysis showed that the variant belonged to an outgroup from other cocadviroids found in monocots.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".