Comprehensive Assessment of Garlic (<i>Allium sativum</i>) Supplement Diet Applications in Aquaculture and Its Effect on Growth Performance, Nutrition Utilization, Body Composition, Microbiome, and Survival in Different Type of Fishes
Bibliographic record
Abstract
Garlic ( Allium sativum ), a bulbous flowering plant in the Allium genus, is celebrated for its diverse culinary and medicinal uses. It's a natural wonder, garlic’s proximate composition records an average of 65% water (compared to over 85% of fresh vegetables), 27.5% carbohydrates, 4.7% fber, 2%~3% organosulfurated compounds, and 2% protein. Scientific studies have revealed garlic's astonishing impact on aquatic life, especially fish. It's shown to significantly boost fish growth, reduce mortality rates, and fortify their antioxidant defenses. Allicin, the standout bioactive compound in garlic, is the driving force behind this transformation. It possesses formidable anti-parasitic properties, confirmed effective against notorious foes like freshwater Ich and marine white spot. This revelation holds promise for revolutionizing aquaculture practices. But garlic's prowess extends further through a treasure trove of organosulfur compounds, including diallyl sulfide, allicin, γ-glutamylcysteine, S-allyl cysteine (alliin), and ajoene. These compounds are associated with various health benefits, from calming inflammation and combating oxidative stress to regulating blood pressure, ameliorating hyperlipidemia, and enhancing endothelial function. In the world of aquaculture, research findings unveil the potential of garlic to be a game-changer. This comprehensive review consolidates existing knowledge on the impact of garlic on fishes. It sheds light on crucial aspects such as growth performance, nutrient utilization, body composition, and survival rates, underscoring garlic supplements' potential to reshape the aquaculture industry. In reviewed studies, garlic supplementation improved weight gain in Nile tilapia by up to 22%, enhanced feed conversion ratio (FCR) by approximately 15%, and increased survival rates to over 95% under bacterial challenge conditions. In rainbow trout, diets containing 2%~3% garlic extract increased body protein content by 6%~8% while reducing lipid deposition by nearly 10%. Similarly, juvenile sturgeon fed 0.5% garlic extract showed a rise in lipid levels from 4.8% to 6.1%, while tilapia fed 3% garlic powder achieved the highest protein levels and the lowest body fat compared to controls. These quantitative outcomes confirm garlic’s measurable role in boosting growth, nutrient utilization, and survival in aquaculture species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".