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Development of efficient and scalable regeneration tissue culture method for Cannabis sativa

2024· article· en· W4403524324 on OpenAlexafffund
Aleksei Sorokin, Igor Kovalchuk

Bibliographic record

VenuePlant Science · 2024
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyCannabis sativaRegeneration (biology)BiotechnologyTissue cultureComputational biologyBotanyCell biologyGeneticsIn vitro

Abstract

fetched live from OpenAlex

Large scale production of uniform disease-free plants is crucial for Cannabis sativa biotechnology. Existing micropropagation protocols rely heavily on shoot multiplication from existing meristems via direct organogenesis. Such protocols do not allow multiplication of plant material through continuous sub-culturing. Protocols that use indirect regeneration are usually not efficient enough and have very low multiplication rates. In the present study, an efficient protocol that uses a combination of direct organogenesis and callogenesis to induce multiple shoot development cultures is developed. Callogenesis was induced from various explants cultured on the media having various combinations of thidiazuron (TDZ) and naphthaleneacetic acid (NAA); best callogenesis and shoot regeneration was achieved from hypocotyl explants cultured on TDZ 0.4 mg l −1 NAA 0.2 mg l −1 . Hypocotyls with cotyledonary node and shoot apical meristem were significantly better for shoot regeneration than explants without it. Shoots obtained from multiple shoot cultures were successfully rooted and then acclimatized under greenhouse conditions to develop into adult cannabis plants. • Large scale micropropagation of Cannabis sativa is difficult. • Direct organogenesis and callogenesis allowed to induce multiple shoot development culture. • Multiple shoot development culture was most efficient from hypocotyl explants cultured on TDZ and NAA. • Various cultivars responded differently to culturing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.347
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes2
Has abstractyes

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