Screening and structural characterization of <i>α</i>-glucosidase inhibitors from hawthorn leaf flavonoids extract by ultrafiltration LC-DAD-MS<sup><i>n</i></sup> and SORI-CID FTICR MS
Bibliographic record
Abstract
In vitro alpha-glucosidase inhibition assays and ultrafiltration liquid chromatography with photodiode array detection coupled to electrospray ionization tandem mass spectrometry (ultrafiltration LC-DAD-ESI-MS(n)) were combined to screen alpha-glucosidase inhibitors from hawthorn leaf flavonoids extract (HLFE). As a result, four compounds were identified as alpha-glucosidase inhibitors in the HLFE, and their structures were confirmed to be quercetin-3-O-rha- (1-4)-glc-rha and C-glycosylflavones (vitexin-2"-O-glucoside, vitexin-2"-O-rhamnoside and vitexin) by high-resolution sustained off resonance irradiation collision-induced dissociation (SORI-CID) data obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS). Several other C-glycosylflavones (vitexin, isovitexin, orientin, isooriention) and their aglycones apigenin and luteolin were evaluated by in vitro assays, and were found to possess strong alpha-glucosidase inhibitory activities as well. Moreover, the substituent groups on the flavones had a great impact on the enzyme inhibition activity. C-3'-OH of the B-ring of flavones in particular increased the alpha-glucosidase inhibition activity, whereas C-glycosylations at C-6 or C-8 of the A ring weakened the inhibition activity.
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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".