A New Approach to Optimizing Propagation and Study of Medicinal Plants In Vitro: Profiling of Endogenous Growth Regulators and Human Neurotransmitters by LC-MS
Bibliographic record
Abstract
Micropropagation allows for propagation of thousands or even millions of true-to-type plants from a range of cell types and tissues. These plants are pathogen-free, and often more uniform than field grown relatives. Additionally, micropropagation technology may be optimized for the production of desirable medicinal compounds. Efficiency of micropropagation depends on a balance of plant growth regulators (PGRs) in the growth medium with auxins inducing root development and cytokinins stimulating shoot formation; unfortunately, not all plants and tissues abide by this keystone principle. Conventional methods to optimize protocols are often costly, time consuming, and involve application of diverse PGRs or inhibitors thereof. We have developed and validated a method for quantification of the main classes of PGRs including three cytokinins, auxin, gibberellic acid, abscisic acid, three jasmonates, and two salicylates via a simple and easily adopted liquid chromatography-mass spectrometry method. It is proposed that by pre-screening tissues it will be possible to better predict ideal starting conditions for establishment of tissue cultures suitable for micropropagation. Additionally, it is often desirable to select starting materials rich in bioactive compounds such as the indoleamines, melatonin and serotonin or other neuroactive compounds such as dopamine, or 5-hydroxytryptophan. Thus, this method has also been modified to allow for quantification of these compounds. We propose a new strategy for the development and implementation of micropropagation protocols using simple, easily modified analytical methods, which may be employed to screen for desirable medicinal plant germplasm and streamline the production of consistent, high quality natural health products.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".