Effect of culture media and auxin on growth and glucosinolate accumulation in the hairy root cultures of mustard (Brassica juncea)
Bibliographic record
Abstract
Brassica juncea is a vegatable that are rich in glucosinolate (GSL) content. The hairy root (HR) cultures system is one of the most useful tools for secondary metabolites (SM) biosynthesis under various growth conditions. In the past, GSLs were mostly used as biopesticides in agriculture, anti-nutritional factors in fodder, and flavors in condiments. However, in recent days, GLSs have received much attention in human health. To investigate the growth response and variation of GSLs accumulation, HRs of mustard were grown in different growth media and auxins. The HRs growth pattern varied largely under the treatments of growth media and auxin. The full-strength SH media responded greatly for achieving the highest dry weight (DW) followed by the ½ SH media and the lowest DW was obtained in full-strength MS media. In all the auxin treatments the HRs production was higher than that of the control. It was noted that at higher NAA and IBA concentrations HR production was increased than that at the lower concentrations. In addition, different growth mediums significantly influenced the GSLs accumulation in mustard HR. The results revealed that ½ B5 media showed the highest total GSLs content followed by B5 and ½ SH. Treatment of mustard HRs with auxins such as IAA and IBA negatively influenced the accumulation of GSLs except for 4-methoxyglucobrassicin. We, therefore, suggest that HRs are a viable option for improving the GSLs content from the HR culture of mustard and that SH and ½ B5 medium provides an alternative approach for mass production of HRs and GSLs in mustard, respectively.
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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 | 0.000 | 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".