The Gene Regulatory Roles of Herbal Extracts on the Growth, Immune System, and Reproduction of Fish
Bibliographic record
Abstract
The crucial need for safe and healthy aquatic animals obligates researchers in aquaculture to investigate alternative and beneficial additives. Medicinal herbals and their extracts are compromised with diverse effects on the performances of aquatic animals. These compounds can affect growth performance and stimulate the immune system when used in fish diet. In addition, the use of medicinal herbs and their extracts can reduce oxidative stress induced by several stressors during fish culture. Correspondingly, aquatic animals could gain increased resistance against infectious pathogens and environmental stressors. Nevertheless, the exact mode of action where these additives can affect aquatic animals' performances is still not well documented. Understanding the mechanistic role of herbal supplements and their derivatives is a vital tool to develop further the strategies and application of these additives for feasible and sustainable aquaculture. Gene-related studies have clarified the detailed information on the herbal supplements' mode of action when administered orally in aquafeed. Several review articles have presented the potential roles of medicinal herbs on the performances of aquatic animals. However, this review article discusses the outputs of studies conducted on aquatic animals fed dietary, medicinal herbs, focusing on the gene expression related to growth and immune performances. Furthermore, a particular focus is directed to the expected influence of herbal supplements on the reproduction of aquatic animals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".