Comparison and Improvement of Total RNA Extraction Methods from Different Tissues of <i>Polygonatum cyrtonema</i> Hua
Bibliographic record
Abstract
Extraction and isolation of high-quality RNA from Polygonatum cyrtonema Hua. is the basis of research on gene expression, regulation and genetic engineering. For screening the best method of total RNA extraction from different tissues of P. cyrtonema, total RNA was extracted from rhizomes, stems, leaves and flowers of P. cyrtonema by six methods that were Trizol method, CTAB-isopropanol method, RNA pure plant kit CTAB-LiCl method, hot phenol method and improved hot phenol method respectively. The concentration and quality of RNA were analyzed using Subordinate function method. The results showed that the improved hot phenol method was the most ideal for RNA extraction among the six methods. The RNA bands of different tissues of P. cyrtonema were complete and clear, the OD260/OD280 and OD260/OD230 values were between 1.8 and 2.1, the extraction concentration values were between 77.16 and 185.72 g/g. This study provide a reference for extracting high-quality total RNA from P. cyrtonema.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".