Antioxidant and Anti-Inflammatory Activities of Kenyan Leafy Green Vegetables, Wild Fruits, and Medicinal Plants with Potential Relevance for Kwashiorkor
Bibliographic record
Abstract
Background. Inflammation, together with related oxidative stress, is linked with the etiology of kwashiorkor, a form of severe acute malnutrition in children. A diet rich in anti-inflammatory and antioxidant phytochemicals may offer potential for the prevention and treatment of kwashiorkor. We selected and assayed five leafy green vegetables, two wild fruits, and six medicinal plants from Kenya for their antioxidant and anti-inflammatory properties. Consensus regarding medicinal plant use was established from ethnobotanical data. Methods. Antioxidant activity and phenolic content were determined using the oxygen radical absorbance capacity (ORAC) assay and Folin-Ciocalteu procedure, respectively. Anti-inflammatory activity was assessed in vitro targeting the inflammatory mediator tumour necrosis factor-alpha (TNF-α). Results. Mangifera indica (leaves used medicinally) showed the greatest antioxidant activity (5940 ± 632 µM TE/µg) and total phenolic content (337 ± 3 mg GAE/g) but Amaranthus dubius (leafy vegetable) showed the greatest inhibition of TNF-α (IC50 = 9 ± 1 μg/mL), followed by Ocimum americanum (medicinal plant) (IC50 = 16 ± 1 μg/mL). Informant consensus was significantly correlated with anti-inflammatory effects among active medicinal plants (r (2) = 0.7639, P = 0.0228). Conclusions. Several plant species commonly consumed by Kenyan children possess activity profiles relevant to the prevention and treatment of kwashiorkor and warrant further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".