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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".