In vitro Anthelmintic Efficacy of Medicinal Plants against Heterakis gallinarum in Village Chickens
Bibliographic record
Abstract
Heterakis gallinarum impedes the productivity of village chickens and hence their socio-economic contribution to rural communties. Smallholder farmers are endowed with vast indigenous knowledge for controlling H. gallinarum and they predominantly use Aloe ferox, Agave sisalana and Gunnera perpensa. However, their anthelminthic efficacy is unknown. Therefore, our objective was to determine the in vitro anthelmintic efficacy of A. ferox, A. sisalana and G. perpensa against H. gallinarum. Heterakis gallinarum worms were recovered from caeca of 20 village chickens. Ten worms were randomly introduced in each of the 42 petri-dishes. Treatments were positive control (distilled water), negative control (mebendazole) and A. ferox, A. sisalana and G. perpensa aqueous extracts at dosage of 7.25, 14.5, 29 and 58 mg/mL. The procedure was done in triplicate. Worm motility and mortality indices were calculated. Among plant, A. sisalana (14.5 mg/mL) had the greatest (80%) worm motility inhibition at 12-h interval (p < 0.05) and the greatest worm mortality index (80%) showing that the plant has anthelmintic properties. Seventy percent of worms were recovered after 48 h following treatment with A. ferox (58 mg/mL). Agave sisalana (14.5 mg/mL) was the most efficient plant in reducing motility and causing mortality of H. gallinarum in vitro. Findings are useful to resource-constrained farmers who use crude extracts, to select the best plant and to pharmaceutical companies for manufacturing anthelmintic drugs.
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".