On‐line coupling pressurised liquid extraction with two‐dimensional counter current chromatography for isolation of natural acetylcholinesterase inhibitors from <i>Astragalus membranaceus</i>
Bibliographic record
Abstract
INTRODUCTION: Radix Astragali, the dried root of Astragalus membranaceus (Fish.) Bge. (family Fabaceae), which is known as Huangqi in China, has been proven to be an immunostimulant, diuretic, antidiabetic, analgesic, and it has also been used as a health food supplement in some Asian populations and also serves as a lead herb in many traditional Chinese medicine formulations as well as in Chinese ethnic tonifying soups. OBJECTIVE: Screening and purification of bioactive compounds from natural products is challenging work due to their complexity. We present the first report on the use of pressurised liquid extraction and on-line two-dimensional counter current chromatography as an efficient medium for scaled-up extraction and separation of six bioactive compounds from Astragalus membranaceus. METHOD: We applied the established method with ultrafiltration-liquid chromatography to screen acetylcholinesterase inhibitors, which were then evaluated and confirmed for anti-Alzheimer activity using PC12 cell model. RESULTS: Six major compounds, namely, calycosin-7-O-β-d-glucoside, pratensein-7-O-β-d-glucoside, formononetin-7-O-β-d-glucoside, calycosin, genistein, and formononetin, with acetylcholinesterase binding affinities were identified and isolated from the raw plant materials via two sets of n-hexane/ethyl acetate/0.2% acetic acid (first-stage counter current chromatography) and n-hexane/ethyl acetate/methanol/water (second-stage counter current chromatography) solvent systems: 1.87:1.0:1.33 and 5.62:1.0:2.42:5.25, v/v/v/v, which were optimised by a mathematical model. CONCLUSION: Therefore, a useful platform for the large-scale production of bioactive and nutraceutical ingredients was developed herein. With the on-line system developed here, we present a feasible, selective, and effective strategy for rapid screening and identification of enzyme inhibitors from complex mixtures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".