Anti-Inflammatory Activities of Chitosan-TPP Nanoformulated Wrightia pubescens R.Br Leaf Extract in Carrageenan-Induced Paw Edema of Wistar Rats
Bibliographic record
Abstract
The use of herbal medicines like riksusu leaves (Wrightia pubescens R.Br) as antiinflammatory agents is limited by the low bioavailability of their active compounds.This herb was traditionally used in East Nusa Tenggara (Indonesia) to treat bruises and purify blood.Nanotechnology, particularly chitosan-NaTPP nanoparticles, offers a promising solution due to their biocompatibility and controlled-release properties.This study evaluated the anti-inflammatory activity of riksusu leaf extract nanoparticles in an in vivo model.The extract was obtained through maceration and analyzed using phytochemical screening and LC-MS/MS.Nanoparticles were synthesized using ionic gelation (extract:chitosan:NaTPP = 1:0.2:1)and characterized using a Particle Size Analyzer and TEM.The extract yield was 12.7%, with 6.84 0.69% moisture content.Phytochemical tests confirmed the presence of saponins, flavonoids, alkaloids, and tannins.LC-MS/MS identified isoindoline, morin, kaempferol, luteolin, and eugenin as active compounds.In vivo testing on carrageenan-induced Wistar rats showed that the nanoparticle group (250 mg/kg BW) achieved an average inhibition of 80.13%, compared to 62.30% (p < 0.05) for the non-nanoparticle extract group.These results demonstrate that nanoparticle formulation significantly enhances the anti-inflammatory potential of riksusu leaf extract.
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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".