Glucoraphanin extraction from <i>Cardaria draba</i>: Part 2. Countercurrent extraction, bioactivity and toxicity testing
Bibliographic record
Abstract
Abstract Glucoraphanin is a potentially valuable plant compound that has shown efficacy in the treatment of hypertension and the removal of carcinogens in animals. Our recent work determined the optimum extraction conditions for the recovery of high amounts of glucoraphanin from the noxious weed Cardaria draba in a single‐staged batch extractor. In this study, a multiple contact, countercurrent flow extraction process was used to achieve further improvements in glucoraphanin recovery. The yield increased by 1.5 times when compared with a single, batch contact extraction. A three‐stage process was sufficient to extract over 90% of the glucoraphanin from C draba at a dry weight plant loading of 50 g dm−3. The experimental results at several solids loadings agreed with the leaching theory and the theoretical model was used to predict the number of stages needed to extract all the glucoraphanin from C draba at high solids loadings. Finally the efficacy and toxicity of the crude glucoraphanin extracts were tested using hepatoma cells. The efficacy was found to be much higher than single‐stage extracts, confirming that cell efficacy is related to the increased quantity of glucoraphanin extracted due to countercurrent operation. The crude extract demonstrated negligible acute toxicity to the hepatoma cells. Copyright © 2005 Society of Chemical Industry
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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) | 0.000 | 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 | 0.000 |
| 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".