Agrobacterium transformation of Rhodiola sp.: current status and limitations
Bibliographic record
Abstract
The study of secondary metabolites has led to the discovery of new drugs for treating human diseases. However, consistent plant supply can be challenging, leading to the use of plant tissue culture techniques such as hairy root culture. Hairy roots have stable genetics, lateral branching, and can produce secondary metabolites, including alkaloids, flavonoids, and terpenoids. Research on hairy roots as a subject began in the late 19th century, and for the last four decades, hairy roots have been utilized for producing secondary metabolites and recombinant proteins. This article focuses on Rhodiola species - genus of perennial plants that belongs to the family Crassulaceae - and its potential as a source of secondary metabolites using hairy root culture techniques. Rhodiola sp. is widely distributed throughout the Arctic regions of the Northern Hemisphere, with several species having significant medicinal properties. The article discusses the possible use of hairy root cultures for the production of Rhodiola secondary metabolites, including salidroside and rosavins, which have demonstrated significant pharmacological activity in various studies. The use of elicitation and genetic engineering techniques to boost secondary metabolite production in Rhodiola hairy roots is also explored. Overall, the article highlights the potential of Rhodiola hairy root cultures as a valuable source of secondary metabolites with medicinal properties. However, despite some studies Rhodiola hairy root induction and culturing still remains highly unexplored.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".