Alpha Mangostin and Xanthone from Mangosteen (Garcinia mangostana L.) Role on Insulin Tolerance and PPAR-γ in Preclinical Model Diabetes Mellitus
Bibliographic record
Abstract
Objective: This research elaborated role of alpha mangostin and xanthone on insulin resistance and peroxisome proliferator–activated receptor (PPAR)-γ by measuring blood glucose level and PPAR- γ expression on adipocyte cell culture. Methods: Insulin tolerance test were conducted using male wistar rat divided into 9 groups, which were normal, control (D-Glucose induced only), glibenclamide, various doses of a-mangostin and xanthone (5, 10, 20 mg/kgbw). All group induced by D-glucose 3 g/kg orally 30 minutes later. Blood glucose levels changes were observed at 90th and 150th minute. While other study observed PPAR-γ expression on adipocyte cell culture that treated with a-mangostin/xanthone/pioglitazone in various concentration. Results: KITT in all treatment groups were significantly different (p<0.05) when compared to the positive control group, except xanthone 5 mg/kgbw. This suggests that a-mangostin 5, 10 and 20 mg/kgbw, xanthone 10 and 20 mg/kgbw, as well as metformin, have the effect of lowering insulin resistance in white rats given a 10-day fatty emulsion. Almost similar with thiazolinedione, alpha mangostin and xanthone increase PPAR-g expression in adipocyte when the concentration bigger. But xanthone effect not as good as α-mangostin or thiazolinedione effect. Conclusion: Alpha mangostin and xanthone are two substances that showed potential effect to improve insulin tolerance by increasing PPAR-g in adipocyte.
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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 | 0.001 |
| 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".