Bibliographic record
Abstract
Pain, inflammation, and fever are interconnected defence responses that, when dysregulated, underpin many diseases. The present study evaluated the ameliorative effect of hydromethanolic extract of Dioscorea bulbifera (HEDB) on pain, fever and inflammation using Wistar rat models. Fresh bulbils of D. bulbifera were dried, pulverised and extracted using 80% aqueous methanol. Twenty-five (25) male Wistar rats (200–250 g) were randomly assigned to five groups (n=5) and used for the study. Group I served as the negative control and received distilled water, group II served as the positive control and was treated with a standard drug, while groups III, IV and V were treated with HEDB at 200, 400, and 800 mg/kg, respectively. Analgesic, anti-inflammatory, and antipyretic tests were performed following established protocols. The present study reveals that oral treatment with HEDB significantly elevated pain threshold, with 200 mg/kg significantly greater than that of the standard drug, Felxicam. In the albumin-induced paw oedema model, HEDB treatment significantly reduced paw diameter at the 3-hour time point, exhibiting a 48.81% inhibition of oedema. This was superior to the 35.70% inhibition observed with 1.5 mg/kg Felxicam. Similarly, treatment with HEDB on yeast-induced pyrexia significantly lowered rectal temperature, with peak effect observed at 400 mg/kg (4.88%), which was lower than the effect of the positive control, Paracetamol (5.97%). These findings demonstrate that hydromethanol extracts of Dioscorea bulbifera exhibit potent analgesic and anti-inflammatory activities, alongside mild antipyretic effect. The evidence from this study provides a scientific validation for its traditional use in managing these conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.830 | 0.781 |
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".