Effect of <I>Kaempferia galanga</I> L. on Xanthine Oxidase Activity and Arterial Stiffness in Hyperuricemia
Bibliographic record
Abstract
Hyperuricemia, characterized by excess uric acid production or reduced uric acid excretion, is a significant risk factor for kidney and cardiovascular diseases. The enzyme xanthine oxidase (XOin) plays a crucial role in uric acid production, and contributes to oxidative stress and inflammation. This study aimed to evaluate the XO inhibitory potential of Kaempferia galanga L. ethanol extract (KGEE) and its effects on arterial stiffness in hyperuricemic rats. Hyperuricemia was induced by placing the animals on 20% fructose in drinking water for 28 days, followed by potassium oxonate injection (4.5 mg/kg, stat). Twenty-five rats were divided into five groups: normal control, positive control (hyperuricemic rats), allopurinol (1.8 mg/kg), and two KGEE groups (50 and 100 mg/kg). After 28 days of treatment, XO inhibitory activity of liver homogenate and serum uric acid concentration were measured. Arterial stiffness was measured on days 0, 14, and 28 using the pulse wave velocity method. XO inhibitory activity of kaempferol a flavonoid in KGEE was assessed in silico by molecular docking simulation. KGEE demonstrated XO inhibitory activity in vitro with IC50 of 31.47 µg/mL. KGEE stimulated XO inhibition in rats’ liver, resulting in percentage inhibition of 79.38% at 100 mg/kg. The extract significantly reduced uric acid levels and improved arterial stiffness, compared to the positive control. In silico molecular docking simulations indicated that kaempferol may act as XO inhibitor. Therefore, KGEE shows promise as an effective XO modulator and a potential biomarker for endothelial dysfunction in hyperuricemia, contributing to the amelioration of arterial stiffness.
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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 | 0.001 |
| 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".