Effect of Intake of Lidah Mertua (Sansevieria trifasciata Laurentii) Leaf Extract on Free Fatty Acid, Glucose and Triglyceride Levels in Obese Wistar Rats and Compound Identification by LC-MS/MS
Bibliographic record
Abstract
Lidah mertua (Sansevieria trifasciata Laurentii) is one of the plants that contains secondary metabolites, namely saponins, flavonoids, sapogenins, tannins, and steroids.The aim of this study was to determine the effect of Lidah mertua leaf extract on triglycerides, glucose, and free fatty acids in obese rats, as well as to identify the compounds.This study used Wistar rats as test animals.There were 4 treatment groups, namely normal control, obesity group, obesity plus LM 100 mg/kg, and obesity plus LM 200 mg/kg.The treatment duration was four weeks, and the parameters measured were body weight, Lee's obesity index, and levels of glucose, free fatty acids, and triglycerides in the serum of Wistar rats, as well as identification of the compounds using LC-MS/MS.The results of the study showed that Lidah mertua leaf extract was able to reduce body weight, Lee's obesity index, and levels of glucose, free fatty acids, and triglycerides with significant differences (p<0.05) from the obese group.The results of LC-MS/MS of Lidah mertua leaf extract identified 15 compounds, namely 3 phenolic acids, 6 flavonoids, a steroid, sapogenin, flavonoid glycosides, tannin, and 2 saponins.This study can be concluded that all identified compounds have a potential role as hypoglycemic and hypolipidemic agents.
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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".