Antidiabetic and Cytotoxic Activities of Rotenoids and Isoflavonoids Isolated from <i>Millettia pachycarpa</i> Benth
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A phytochemical investigation of the root and leaf extracts of Millettia pachycarpa Benth resulted in the isolation and identification of 16 compounds, including six rotenoids ( 1 – 6 ) and 10 prenylated isoflavonoids ( 7 – 16 ). Compound 4 was isolated as a scalemic mixture, which was resolved by chiral HPLC to afford (−)-(6a S,12a S )-12a-hydroxy-α-toxicarol ( 4 ) and (+)–(6a R,12a R )-12a-hydroxy-α-toxicarol ( 4 ). (+)-(6a R,12a R )-Millettiapachycarpin ( 3 ) and (−)-(6a S,12a S )-12a-hydroxy-α-toxicarol ( 4 ) were isolated as new compounds. The absolute configuration of (−)-(6 R )-pachycarotenoid ( 2 ), (+)–(6a R,12a R )-millettiapachycarpin ( 3 ), (−)-(6a S,12a S )- 4 and (+)–(6a R,12a R )-12a-hydroxy-α-toxicarol ( 4 ), (+)-(6a S,12a S )-( 5 ), and (−)-(6a S,12a S,2″ R )-sumatrol ( 6 ) were identified by electronic circular dichroism (ECD) data. (−)-(6a S,12a S,2″ R )-Sumatrol ( 6 ) was also confirmed by X-ray diffraction analysis using Cu–Kα radiation. Antidiabetic activities, including α-glucosidase and α-amylase inhibitory activities, and cytotoxicities against lung cancer A549, colorectal cancer SW480, and leukemic K562 cells of some isolated compounds were evaluated. Of these, isolupalbigenin ( 11 ) exhibited the highest α-glucosidase inhibitory activity, with an IC 50 value of 11.3 ± 0.2 μM, whereas the scalemic mixture of 12a-hydroxy-α-toxicarol ( 4 ) displayed the best α-amylase inhibitory activity, with an IC 50 value of 106.9 ± 0.2 μM. Euchrenone b10 ( 15 ) exhibited the highest cytotoxicity against lung cancer A549, colorectal cancer SW480, and leukemic K562 cells, with IC 50 values of 40.3, 39.1, and 15.1 μM, respectively. In addition, molecular docking simulations of α-glucosidase inhibition of the active compounds were studied.
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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) | 0.000 | 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) | 0.002 | 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".