Preventive Effect of Ethanol Extract of Red Spinach (Amaranthus tricolor L.) on Diet-induced Obese Zebrafish
Bibliographic record
Abstract
Background: Nowadays obesity recognized as chronic or non-communicable disease. Pathophysiology of obesity caused by an imbalance between energy intake and expenditure. Obesity was known to be a risk factor for the development of metabolic syndrome. The aim of this study is to evaluate activity of ethanol extract of red spinach (EERS) to prevent obesity in diet-induced zebrafish. Materials and Methods: Acclimatization period for zebrafish was carried out for 2 weeks. After the acclimatization, zebrafish were divided into 6 groups (n = 10 in each group) such as normal group (negative control); obese group (positive control); standard drug (orlistat 4,5 µg/ml); EERS group (50 µg/ml); EERS group (100 µg/ml); and EERS group (200 µg/ml). During a period of 4 weeks, normal group received a standard diet and didn’t received EERS administration. Positive control group received Artemia. Treated group received Artemia and were combined by administration of EERS. To determine obesity criteria we calculated of zebrafish BMI. Results: Based on BMI calculation, EERS 50 µg/ml, 100 µg/ml and 200 µg/ml showed a preventive effect on obesity compared to the positive control group. In addition, EERS 50 µg/ml was able to reduce BMI lower than the other extract groups. Conclusion: It can be concluded that EERS 50 µg/ml has strength preventive effect on diet-induced obese zebrafish. This effect might be influenced by the presence of phytochemical compounds of extract such as flavonoid, saponins, and tannins.
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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 | 0.000 | 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) | 0.001 | 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".