Ethnopharmacological Properties of African Medicinal Plants for the Treatment of Neglected Tropical Diseases
Bibliographic record
Abstract
Agriculture is considered the primary source of income and livelihood in Africa. It is rational for people to look around their environment for food and medicine. The African legacy regarding the traditional use of medicinal plants is vast and diverse, due to cultural myths and economic logic. This review briefly defines the neglected tropical diseases and surveys African medicinal plants used for neglected tropical diseases. In Africa, people may share several plants for similar diseases, e.g., Nicotiana tabacum L. and Ricinus comminus L. are used for treating Buruli ulcer infection. Folkloric African plants for the treatment of bacterial, fungal, and viral neglected tropical diseases are listed and reported in the first parts. Medicinal plants for curing parasitic neglected tropical diseases are tabulated. A plethora of medicinal plants and bioactive compounds and their preparation methods, such as macerations and boiling are reported. This report reflects the richness of Africa with medicinal plants and herbal preparations being used for the treatment of various diseases, including neglected tropical diseases. Scientific investigation of these plants has yet to be conducted to isolate the active components and determine any toxic activities. Besides, knowledge of the mechanism of action behind these beneficial effects is highly required. This review will draw the attention of pharmaceutical companies and research institutions to examine the plants presented here for further laboratory analysis and experiments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".