The use of African medicinal plants in cancer management
Bibliographic record
Abstract
Cancer is the third leading cause of premature death in sub-Saharan Africa. Cervical cancer has the highest number of incidences in sub-Saharan Africa due to high HIV prevalence (70% of global cases) in African countries which is linked to increasing the risk of developing cervical cancer, and the continuous high risk of being infected with Human papillomavirus In 2020, the risk of dying from cancer amongst women was higher in Eastern Africa (11%) than it was in Northern America (7.4%). Plants continue to provide unlimited pharmacological bioactive compounds that are used to manage various illnesses, including cancer. By reviewing the literature, we provide an inventory of African plants with reported anticancer activity and evidence supporting their use in cancer management. In this review, we report 23 plants that have been used for cancer management in Africa, where the anticancer extracts are usually prepared from barks, fruits, leaves, roots, and stems of these plants. Extensive information is reported about the bioactive compounds present in these plants as well as their potential activities against various forms of cancer. However, information on the anticancer properties of other African medicinal plants is insufficient. Therefore, there is a need to isolate and evaluate the anticancer potential of bioactive compounds from other African medicinal plants. Further studies on these plants will allow the elucidation of their anticancer mechanisms of action and allow the identification of phytochemicals that are responsible for their anticancer properties. Overall, this review provides consolidated and extensive information not only on diverse medicinal plants of Africa but on the different types of cancer that these plants are used to manage and the diverse mechanisms and pathways that are involved during cancer alleviation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".