Growth Regulators Affect the Growth and Biochemical Activity of Curcuma longa Plants Grown in vitro
Bibliographic record
Abstract
This study aimed to evaluate the in vitro growth and biochemical activity of Curcuma longa explants using different MS medium formulations and growth regulators. In all the experiments, plants were grown in MS medium supplemented with agar (6.5 g L-1) and pH adjusted to 5.8. In the first assay, the MS culture medium at 70% strength, supplemented with 30 g L-1 of sucrose, and without the addition of activated carbon, resulted in the highest number of shoots. The sucrose concentration of 60 g L-1, combined with the addition of actived charcoalin half-strength MS medium, resulted in the increased root dry mass, root collar diameter, and relative chlorophyll index. In the second assay, the highest root collar diameter and dry matter of shoots and roots were found in the MS medium supplemented with 4.44 BAP, 0.46 KIN, and 1.08 NAA. The MS medium with 8.88 BAP, 0.92 KIN, and 2.16 μM NAA resulted in the highest number of shoots (7.75), number of leaves (35), and shoot length (88.57 mm). The antioxidant activity was significantly higher in the treatments that resulted in better plantlets growth performance, demonstrating that the antioxidant activity is related to other factors such as a possible role of growth regulators on the elicitation of compounds in plants. Superoxide dismutase had a high enzymatic activity in both assays, whereas the enzymatic activity of catalase and ascorbate peroxidase was dependent on the culture media used.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".