Antidiabetic Activity of Sungkai (Peronema canescens Jack) Leaves Ethanol Extract on the Male Mice Induced Alloxan Monohydrate
Bibliographic record
Abstract
Sungkai (Peronema canescens Jack) is an alternative natural ingredient that can be used as an antidiabetic containing several secondary metabolites which are thought to reduce blood glucose levels by inhibiting the action of the α-glucokinase enzyme and increasing the activity of antioxidant enzymes and being able to regenerate β-cells, damaged pancreas so that insulin deficiency can be overcome. The aim of this study was to determine the effect of P. canescens leaves ethanol extract on antidiabetic activity and to determine the effect of various doses of sungkai leaf ethanol extract as antidiabetic. The scientific research method used in this study was a completely randomized design (CRD) and data analysis used in this study is the one way ANOVA, following by the Duncan test. Blood glucose measurement in treated mice was carried out 3 times. Mice were devided into five groups, three mice in each group, and each group was administered different dosages of sungkai extract (T1 = extract 175 mg/kg body weight; T2 = extract 350 mg/kg body weight; T3 = extract 700 mg/ kg body weight). First, the mice were acclimatized for six days and the injection of treatment was started at day 7. Blood glucose levels, urine volume, body weight and daily drinking of mice was carried out before induction (day 0), after induction (day 8) and after the end of treatment for 10 days (day 18), was carried out using the Point of Care Test (POCT) method using the Easy Touch GCU. The results showed that the ethanol extract of sungkai leaves had the potential to reduce blood glucose levels in mice. The ethanol extract of sungkai leaves at a dose of 350 mg/kg body weight has the most optimal potential to reduce blood glucose levels, urine volume, daily drinking, and body weight.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".