Cytotoxic Properties on Cervical and Liver Cancer Cells of Two Plant Recipes from Burkina Faso
Bibliographic record
Abstract
Cancer is one of the deadliest diseases in developing countries. In recent years, natural plant-based compounds have been used in the search for drugs to combat numerous diseases, including cancer. In this study, we evaluate the cytotoxic properties of paanfo tiben 1 and paanfo tiben 2, two traditional herbal formulations from Burkina Faso used in the treatment of cancer in Burkina Faso. To this end, the recipes were infused and freeze-dried. The dry extracts obtained were used to determine total phenolics and flavonoids content, assess antioxidant activity using the DPPH, ABTS and FRAP methods, evaluate anti-inflammatory properties by inhibiting 15-LOX, COX 1 and 2, and assess cytotoxic activity on HeLa cervical cancer and HePG2 liver cancer cell lines using the MTT test. The paanfo tiben 1 recipe showed the highest levels of total phenolics and flavonoids, as well as the best antioxidant activities, with IC50 values of 21.020 ± 0.6 µg/ml and 22.94 ± 0.57 µg/ml for DPPH and ABTS, and 165.15 mM EAA/mg dry extract for FRAP. It also exhibited the best cytotoxic activity with IC50 values of 112.02 ± 0.025 µg/ml on HeLa cells and 80.67 ± 6.08 µg/ml on HepG2 cells. On the other hand, paanfo tiben 2 exhibited the best anti-inflammatory activities through inhibition of 15-LOX and COX 1, with inhibition percentages at 100 µg/ml of 32.523% and 24.717 % respectively. These results could justify the traditional use of these two recipes by traditional health practitioners in the treatment of cancer sufferers in Burkina Faso.
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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 | 0.000 |
| 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) | 0.002 | 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".