Medical Cannabis and Industrial Hemp Tissue Culture: Present Status and Future Potential
Bibliographic record
Abstract
L.) have gained immense attention in medical, food, and a plethora of other consumer product markets. Among the planting materials used for cultivation, tissue culture clones provide various advantages such as economies of scale, production of disease-free and true-to-type plants for reducing the risk of GMP-EuGMP level medical cannabis production, as well as the development and application of various technologies for genetic improvement. Various tissue culture methods have the potential application with cannabis for research, breeding, and novel trait development, as well as commercial mass propagation. Although tissue culture techniques for plant regeneration and micropropagation have been reported for different cannabis genotypes and explant sources, there are significant variations in the response of cultures and the morphogenic pathway. Methods for many high-yielding elite strains are still rudimentary, and protocols are not established. With a recent focus on sequencing and genomics in cannabis, genetic transformation systems are applied to medical cannabis and hemp for functional gene annotation via traditional and transient transformation methods to create novel phenotypes by gene expression modulation and to validate gene function. This review presents the current status of research focusing on different aspects of tissue culture, including micropropagation, transformation, and the regeneration of medicinal cannabis and industrial hemp transformants. Potential future tissue culture research strategies helping elite cannabis breeding and propagation are also presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".