Unveiling the anti-diabetic effects of <i>Garcinia hombroniana</i> fruit pericarp and leaf <i>: in vitro</i> radical scavenging, alpha-glucosidase inhibition, pharmacokinetics, and <i>in silico</i> molecular docking of Q-TOF-LCMS identified compounds
Bibliographic record
Abstract
Garcinia hombroniana is traditionally used to treat diabetes in Malaysia. However, scientific studies have yet to identify the phytoconstituents responsible for its blood glucose-lowering effects. This study aimed to evaluate the antioxidant and α-glucosidase inhibitory effects of G. hombroniana fruit pericarp and leaf dichloromethane (DCM) and ethanol (EtOH) extracts and identify the active principles. The extracts were analysed for their total phenolic and flavonoid contents (TPC, TFC), in vitro antioxidant activity via radical scavenging, and α-glucosidase inhibitory effect. Additionally, QTOF-LCMS analysis using negative and positive polarisation modes was conducted to identify potential α-glucosidase inhibitors. The identified compounds underwent comprehensive screening, including pharmacokinetics, drug-likeness rules, PASS analysis, and toxicity assessments. Their α-glucosidase inhibitory effect was further validated using a molecular docking approach. All extracts showed significant TPC and TFC values, as well as antioxidant effects. Notably, DCM fruit pericarp (4.11 ± 0.91 μg/mL), DCM leaf extract (1.49 ± 0.68 μg/mL), and ethanolic leaf extract (2.21 ± 0.50 μg/mL) exhibited potent α-glucosidase inhibitory effects, surpassing quercetin (5.11 ± 1.07 μg/mL) used as a standard. The QTOF-LCMS analysis identified 191 compounds, of which 19 were selected for in silico molecular docking. These compounds were confirmed as α-glucosidase inhibitors and suggest that G. hombroniana extracts may be used for managing postprandial glucose in diabetic patients.
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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) | 0.001 | 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".