Exploring two bioreactor systems for micropropagation of Vaccinium membranaceum and the antioxidant enzyme profiling in tissue culture-raised plants
Bibliographic record
Abstract
Abstract Vaccinium membranaceum (black huckleberry) is known for its high content of bioactive compounds. This study introduces a novel approach for bioreactor micropropagation using stationary (Growtek) and temporary immersion (RITA) bioreactor systems using a liquid nutrient medium to enhance the in vitro propagation of black huckleberry. Adventitious shoot regeneration from leaf explants reached 80% efficiency in a semi-solid nutrient medium with 10 μM thidiazuron. Shoot proliferation was optimized in liquid culture, where the Growtek system yielded the most robust shoot regeneration at 76% and second highest was observed on RITA at 40.33%. Morphological and histological analyses of different cultured explants revealed various stomatal density, trichome formation and mesophyll tissue organization. Biochemical profiling of antioxidant enzymes showed that greenhouse grown leaves had higher levels of bioactive compounds, such as flavonoids, proanthocyanins, and antioxidant activity. However, Growtek-cultured leaves exhibited elevated phenolic content and catalase activity. Additionally, antioxidant enzymes, including catalase (2.8 U g −1 min −1 flw) and superoxide dismutase (0.80 U g −1 min −1 flw), were higher in Growtek and leaf-cultured shoots, respectively. The greenhouse grown plants demonstrated the highest overall phytochemical activity, with the most abundant proanthocyanins (8.3 mg g −1 flw) and anthocyanins (26 mg g −1 flw). This work presents a highly efficient liquid micropropagation protocol for black huckleberry with insights into its bioactive compounds and antioxidant enzyme profiles, highlighting its potential in commercial production and medicinal applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".