Self-Nanoemulsifying Drug Delivery System (SNEDDS) for Antidiabetic Formulation from Mahogany Seeds (Swietenia Mahagoni Jacq) and Moringa oleifera
Bibliographic record
Abstract
This study aims to produce a Self-Nanoemulsifying Drug Delivery System (SNEDDS) formulation of containing a combination of mahogany seed and moringa leaf extracts with antidiabetic activity.The research was conducted by extracting mahogany seeds via Soxhlet extraction and moringa leaves via maceration.SNEDDS was prepared by mixing mahogany seed and moringa leaf extracts with surfactant (Tween 80) and cosurfactant (PEG 400), followed by characterization using a Particle Size Analyzer (PSA).Furthermore, the antidiabetic activity test of SNEDDS formulation of mahogany seed extract and moringa leaf was conducted in vivo using male mice.The antidiabetic activity test in mice was conducted by administering the SNEDDS formulation via the oral route.Thirty-five male mice (Mus musculus L.) were induced with hyperglycemia using alloxan and served as the study's experimental animals.All mice were divided into 7 groups, namely positive control group, negative control group, mahogany seed extract SNEDDS group (SM), moringa leaf SNEDDS (SO).The results show the SNEDDS formula group of mahogany seed extract and moringa leaf which has the best characterisation of the test results.The SNEDDS formulation treatment had an average percentage of decrease in blood sugar levels which was quite high in the SM and SO formula with a ratio of 1:1 of 33.36±7.14, this formula was slightly higher than the ratio of 1:2 of 32.26±6.95.ANOVA test results show a significance value of 0.01 (p<0.05) which means there is a difference in influence between treatments.These findings suggest that the SNEDDS formulation containing mahogany seed and moringa leaf extracts demonstrates significant antidiabetic potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".