Anti-hemolytic, Anti-lipid Peroxidation and Antioxidant Properties of Three Plants Locally Used to Treat Metabolic Disorders: Allium sativum, Persea americana and Citrus sinensis
Bibliographic record
Abstract
Citrus sinensis, Persea americana and Allium sativum are good sources of large number of various bioactive substances including antioxidants. Antioxidants play an essential role in the prevention and treatment of type 2 diabetes and other metabolic diseases. This study was conducted to evaluate and compare the anti- lipid peroxidation, anti-hemolytic and antioxidant capacity of aqueous extracts of the three plants in vitro. Polyphenolic and flavonoids content of aqueous extracts (EA) of plants were determined. The anti-lipid peroxidation was evaluated on homogenates of rat liver. Anti-haemolytic effects were assessed using erythrocyte system model induced free radicals from human red blood cells. Total antioxidant capacity and antiradical or scavenging capacity were evaluated on biological free radical systems (OH, NO.) and synthetic radicals (DPPH and ABTS+). Results showed that, different extracts possess variable amounts of polyphenolic compounds. They exhibited significant anti-hemolytic and anti-lipid peroxidation activities. Aqueous extracts of Citrus sinensis are powerful scavenger of OH. (IC50=1.05x10-3mg/ml) and NO. (IC50= 1.29x10-3 mg/ml) free radicals while Persea americana seed extract possesses the highest anti-lipid peroxidation capacity (59.72%) and the best scavenging capacity against DPPH and ABTS. Red blood cells were highly protected (at 97.87%, p<0.05) by Allium sativum extract at 1mg/mL and Citrus sinensis (87.7%, p<0.05). Therefore, anti-hemolytic, anti-lipid peroxidation effects of the aqueous extracts of the three plants can justify their use and efficacy in alternative treatment of metabolic diseases.
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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) | 0.001 | 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".