A Modified Method of Total RNA Isolation for Mango Leaf Tissues
Bibliographic record
Abstract
Complex metabolic components in mango leaves lead to high difficulty in RNA extraction. In order to improve the quality of total RNA extraction, on the basis of the existing methods reported in the literature, an RNA extraction method combining acetone washing liquid nitrogen abrasive material with 0.3 mol/L lithium chloride (LiCl) solution and 2 times the volume of anhydrous ethanol as the second precipitator was developed. The experimental results showed that the improved method significantly reduced the amount of impurity precipitation in the RNA extraction process. The electrophoretic bands of total RNA extracted were complete. The absorbance ratio of OD 260/280 was about 1.9, and the average was 329.8 g/g FW±11.2 g/g FW. Further validation experiments of RNA reverse transcription and the polymorphic amplification of double-stranded cDNA related sequences (RAP-PCR) obtained clear polymorphic bands, showing that the total RNA from mango leaves had high quality and was suitable for molecular biology experiments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".