Semi-Quantitatif Phytochemical Profile and Antiradical Potential of Aqueous and 70 % Ethanol Extracts of Zanthoxylum Zanthoxyloides (Lam) Zepern and Timler, Leaves Used In Traditional Medicine in the North of Cote D'ivoire
Bibliographic record
Abstract
Herbal medicines have grown in popularity in recent decades, with many people turning to them to maintain their health and prevent and treat various illnesses, particularly chronic diseases.In Europe, the use of traditional medicine varies from 42% in Belgium to 90% in the UK, and from 42% in the USA among adults to 70% in Canada (Sarman et Uzuntarla 2022; Esmal, 2017).In Africa, the use of traditional medicine varies from 60% in Uganda and the United Republic of Tanzania, 70% in Ghana and Rwanda, to 80% in Benin and 90% in Burundi and Ethiopia (Ouoba et al., 2022).For millions of people, herbal medicines are the only reliable source of healthcare.This is due to the safety of traditional medicines its easy access and affordability, in contrast to the high cost of health worker services and pharmaceutical drugs.The elimination of natural molecules by the body is easy, and the risk of carcinogenicity or complicated diseases is low.Many therapeutic virtues are attributed to plants, which are generally linked to their richness in phytomolecules known as secondary metabolites (Jamshidi-Kia et al, 2017).These secondary metabolites, which contain several classes of compounds, are generally of polyphenolic origin and are the most abundant in plant material (Kouam et al., 2021).These polyphenolic compounds are endowed with significant antimicrobial, antiplasmodic, antihelmetic, antihistaminic and antioxidant activities (Kouadio et al., 2021).As such, they have the potential to treat many emerging chronic pathologies linked to oxidative stress, such as cancer, diabetes, asthma,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".