Combination of integrated expanded bed adsorption chromatography and countercurrent chromatography for the direct extraction and purification of pseudohypericin and hypericin from St. John's wort (<i>Hypericum perforatum</i> L.)
Bibliographic record
Abstract
St. John's wort has attracted particular attention because of its beneficial effects as an antidepressant, antiviral, and anticancer agent. A method for the combination of integrated expanded bed adsorption chromatography and countercurrent chromatography for the simultaneous extraction and purification of pseudohypericin and hypericin from the herb is presented in this paper. Firstly, the constituents were extracted and directly adsorbed by expanded bed adsorption chromatography under optimal conditions. The stepwise elution was then performed by expanded bed adsorption chromatography that enriched the targets with higher purities and recoveries compared to other methods. Secondly, the eluent fractions from expanded bed adsorption chromatography were further separated by two-step high-speed countercurrent chromatography. A two-step high-speed countercurrent chromatography method with a biphasic solvent system composed of n-hexane/ethyl acetate/methanol/water with a volume ratio of 1:2:1:2 was performed by stepwise changing the flow rate of the mobile phase. Consequently, 5.6 mg of pseudohypericin and 2.2 mg of hypericin with purities of 95.5 and 95.0%, respectively, were successfully obtained from 40 mg of crude sample.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".