Comparative Different DNA Isolation Protocols from Ziziphus spina-christi (L.) Leaves through RAPD and ISSR Markers
Bibliographic record
Abstract
Genomic analysis of plants relies on high quantity and quality of pure DNA. Extraction and purification of DNA from woody and medicinal plants, such as fruit trees present a great challenge due to accumulation of a large amount of co-purify with DNA, including polysaccharides, polyphenols and proteins. Therefore, it is necessary to optimize the extraction protocols to reduce these compounds to the lowest level. A study was conducted to compare six DNA extraction and precipitation methods for genomic analysis in Ziziphus spina-christi (L.) plant tissues. The results showed significant differences in DNA contents among the six methods. Quantity and quality of extracted genomic DNAs were compared by employing the spectrophotometer, Nano-Drop, agarose gel electrophoresis, digestion by restriction enzymes and polymerase chain reaction (PCR) methods and molecular marker such as RAPD and ISSR. The method of Vroh Bi et al., provided the best results (208.89 ng/μL) in terms of quantity and quality of DNA, and Doyle and Doyle method as second method for leaves sample were chosen. According to the results, the method of Bi et al. is recommended for DNA extraction from plant tissues having high level of polysaccharides and phenol compounds.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".