Novel Functional Food Properties of Forest Onion (Eleutherine bulbosa Merr.) Phytochemicals for Treating Metabolic Syndrome: New Insights from a Combined Computational and In Vitro Approach
Bibliographic record
Abstract
Metabolic syndrome is a global health problem. The use of functional foods as dietary components has been increasing. One food of interest is forest onion extract (FOE). This study aimed to investigate the effect of FOE on lipid and glucose metabolism in silico and in vitro using the 3T3-L1 mouse cell line. This was a comprehensive study that used a multi-modal computational network pharmacology analysis and molecular docking in silico and 3T3-L1 mouse cells in vitro. The phytochemical components of FOE were analyzed using untargeted ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS). Next, an in silico analysis was performed to determine FOE’s bioactive compounds, and a toxicity analysis, protein target identification, network pharmacology, and molecular docking were carried out. FOE’s effect on pancreatic lipase, α-glucosidase, and α-amylase inhibition was determined. Finally, we determined its effect on lipid accumulation and MAPK8, PPARG, HMGCR, CPT-1, and GLP1 expression in the preadipocyte 3T3-L1 mouse cell line. We showed that the potential metabolites targeted glucose and lipid metabolism in silico and that FOE inhibited pancreatic lipase levels, α-glucosidase, and α-amylase in vitro. Furthermore, FOE significantly (p < 0.05) inhibits targeted protein expressions of MAPK8, PPARG, HMGCR, CPT-1, and GLP-1 in vitro in 3T3-L1 mouse cells in a dose-dependent manner. FOE contains several metabolites that reduce pancreatic lipase levels, α-glucosidase, α-amylase, and targeted proteins associated with lipid and glucose metabolism in vitro.
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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 | 0.000 | 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) | 0.000 | 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".