Capillary Zone Electrophoresis Method for the Separation of Glucosidase Inhibitors in Extracts of <i>Salacia reticulata</i>, a Plant Used in Ayurvedic Treatments of Type-2 Diabetes
Bibliographic record
Abstract
A simple and reproducible capillary-zone electrophoresis (CZE) method was developed for the separation and quantitation of sulfonium-ion-containing compounds isolated from plants of the Salacia genus which are traditionally used in Ayurvedic medicine for the treatment of type-2 diabetes. The method sufficiently resolved four different compounds with confirmed glucosidase inhibitory activity, namely, salacinol, ponkoranol, kotalanol and de-O-sulfonated kotalanol. Separation could be achieved in less than 9 min, and calibration curves showed good linearity. Detection limits were determined to be in the low mug/mL range. This method was used to demonstrate that de-O-sulfonated kotalanol isolated from natural sources has identical ionic mobility to a synthetic standard. Furthermore, new extraction conditions were developed by which the zwitterionic compounds (salacinol, ponkoranol, and kotalanol) could be separated from de-O-sulfonated kotalanol in a single solid-phase extraction (SPE) procedure. The extraction gave reproducibly high recoveries and was used to process four commercial Salacia extracts for CZE analysis to reduce the complexity of resulting electropherograms and to facilitate the detection of the four inhibitors in question. De-O-sulfonated kotalanol was detected in two of four Salacia samples while ponkoranol was present in all four. A comparison of all samples tested demonstrated that they had remarkably similar patterns of peaks, suggesting that this CZE method may be useful in the chemical fingerprinting of Salacia-containing products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".