Methanolic Extract of Edible <i>Lasia spinosa</i> Rhizome: A Potential Natural Source of Analgesic, Diuretic, and Thrombolytic Agents
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Bibliographic record
Abstract
Kohila (Lasia spinosa), a marshy plant with spiky rhizomes, is traditionally used in ethnomedicine to treat ailments like uterine cancer, arthritis, inflammation, and gastrointestinal disorders while also being consumed as a vegetable. This study evaluated the phytochemical composition and bioactive potential of methanolic rhizome extract (LSR-ME) through qualitative and quantitative screening, along with analgesic, diuretic, and thrombolytic activity assays. Phytochemical analysis confirmed the presence of flavonoids, alkaloids, glycosides, tannins, and carbohydrates. In analgesic tests, LSR-ME at 400 mg kg−1 showed significant pain inhibition (47.73% in acetic acid–induced writhing and 37.83% in formalin–induced writhing). It also demonstrated notable diuretic effects, with Lipschitz values confirming its activity (p < .05). The extract exhibited strong, dose-dependent thrombolytic activity (p < .001). Molecular docking studies have highlighted meridinol’s superior binding efficiency (−8.5 to −9.2 kcal mol−1) and high affinity for ligand–protein interactions. Computational AdmetSAR analysis further supported the therapeutic potential of the identified compounds. Overall, the findings from in vivo, in vitro, and molecular docking studies indicate that LSR-ME has promising analgesic, diuretic, and thrombolytic properties, warranting further investigation into its medicinal applications. These results validate traditional uses of L. spinosa and highlight its potential as a source of bioactive compounds for drug development.
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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) | 0.001 | 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 it