An Arylbenzofuran, Stilbene Dimers, and Prenylated Diels–Alder Adducts as Potent Diabetic Inhibitors from Morus bombycis Leaves
Bibliographic record
Abstract
Morus bombycis has a long history of usage as a treatment for metabolic diseases, especially, diabetes mellitus (DM). Thus, we aimed to isolate and evaluate bioactive constituents derived from M. bombycis leaves for the treatment of DM. According to bioassay-guided isolation by column chromatography, eight compounds were obtained from M. bombycis leaves: two phenolic compounds, p-coumaric acid (1) and chlorogenic acid methyl ester (2), one stilbene, oxyresveratrol (3), two stilbene dimers, macrourin B (4) and austrafuran C (6), one 2-arylbenzofuran, moracin M (5), and two Diels–Alder type adducts, mulberrofuran F (7) and chalcomoracin (8). Among the eight isolated compounds, the anti-DM activity of 3–8 (which possess chemotaxonomic significance in Morus species) was evaluated by inhibition of α-glucosidase, protein tyrosine phosphatase 1B (PTP1B), human recombinant aldose reductase (HRAR), and advanced glycation end-product (AGE) formation as well as by scavenging peroxynitrite (ONOO−), which are crucial therapeutic targets of DM and its complications. Compounds 4 and 6–8 significantly inhibited α-glucosidase, PTP1B, and HRAR enzymes with mixed-type and non-competitive-type inhibition modes. Furthermore, the four compounds had low negative binding energies in both enzymes according to molecular docking simulation, and compounds 3–8 exhibited strong antioxidant capacity by inhibiting AGE formation and ONOO− scavenging. Overall results suggested that the most active stilbene-dimer-type compounds (4 and 6) along with Diels–Alder type adducts (7 and 8) could be promising therapeutic and preventive resources against DM and have the potential to be used as antioxidants, anti-diabetic agents, and anti-diabetic complication agents.
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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".