The most commonly used spices in Thai traditional medicine: in vitro evaluation of anti-hyperglycemic, antioxidant, polyphenol content, and nitric oxide production inhibitory activities
Bibliographic record
Abstract
Background and purpose: Diabetes mellitus is a persistent hyperglycemic condition. Thai cuisine and medicine incorporate spices: nutmeg, mace, clove buds, cardamom, cinnamon, and coriander. The in vitro impacts of these spices on anti-diabetic, antioxidant, anti-inflammatory, and total phenolic and flavonoid content were assessed. Experimental approach: Alpha-amylase and alpha-glucosidase inhibition assays were conducted. Antioxidant potential was measured through DPPH and ABTS assays. Anti-inflammatory activity was determined by inhibiting nitric oxide generation in RAW 264.7 cells. Total phenolic content was quantified using the Folin Ciocalteu method, while total flavonoid content was estimated via the aluminum chloride colorimetric method. Findings/Results: Ethanolic and aqueous extracts of a blend of spices (Siam cardamom, nutmeg, mace, and clove buds), denoted as 4-GlurE and 4-GlurA, displayed concentration-dependent inhibition of alpha-glucosidase, with IC 50 values of 0.373 and 0.435 mg/mL, respectively. 4-GlurE and 4-GlurA exhibited antioxidant activity, by ABTS ·+ radical and DPPH scavenging capabilities. 4-GlurE demonstrated anti-inflammatory potential by reducing nitric oxide generation (IC 50 : 43.95 ± 2.47 μg/mL). 4-GlurE and 4-GlurA possessed total phenolic content (TPC) of 122.47 ± 1.12 and 148.72 ± 0.14 mg GAE/g, respectively. 4-GlurE exhibited a higher total flavonoid content (TFC) compared to the aqueous extract (340.33 ± 4.77 and 94.17 ± 3.36 mg QE/g). Cinnamon and clove aqueous extracts were more potent than acarbose in alpha-glucosidase inhibition with the highest antioxidant activity. Polyphenol levels (TPC and TFC) exhibited strong correlations with antioxidant capacity. Conclusions and implications: Findings are consistent with the traditional use of 4-Glur, with cinnamon, for diabetes prevention and treatment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".