Phytochemical composition, nutritional values, traditional uses of Tetrapleura tetraptera and Ricinodendron heudelotii and their pharmacological activities: an update review
Bibliographic record
Abstract
The exploration of the use of natural herbals drug, especially plant parts, is a major area of focus. Medicinal plants are being increasingly used to manage a wide numerous ailment. Tetrapleura tetraptera and Ricinodendron heudelotii are known among medicinal plants having beneficial effects due to their several biological activities. The fruits and seeds of these plants are mostly used in many parts of Africa as spice for flavoring soup and making stews. This update review focused on the phytochemical characterization, traditional used, nutritional values and biological activities of these species. Both are known to possess macro-and micro-nutrients. They are rich in majority to the phytochemical’s compounds including polyphenols, flavonoids, tannins, reducing sugar, saponins and alkaloids etc. Their activities have been reported positive by several authors for both alcoholic and aqueous extractions. The plants exhibited appreciable antioxidant, anti-inflammatory and good antimicrobial activities against the test microorganisms justifying their broad-spectrum use. The hypolipidaemian, hypocholesterolaemian and hypoglycaemic properties have been proven to be efficacy especially for the plant Tetrapleura tetraptera and much practice remains to be done with Ricinodendron heudelotii. The confirmation of these biological activities is related to the high content of bioactive molecules conferring beneficial properties to plants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".