Antimicrobial Compounds from Eukaryotic Microalgae against Human Pathogens and Diseases in Aquaculture
Bibliographic record
Abstract
The search for novel compounds of marine origin has increased in the last decades for their application in various areas such as pharmaceutical, human or animal nutrition, cosmetics or bioenergy. In this context of blue technology development, microalgae are of particular interest due to their immense biodiversity and their relatively simple growth needs. In this review, we discuss about the promising use of microalgae and microalgal compounds as sources of natural antibiotics against human pathogens but also about their potential to limit microbial infections in aquaculture. An alternative to conventional antibiotics is needed as the microbial resistance to these drugs is increasing in humans and animals. Furthermore, using natural antibiotics for livestock could meet the consumer demand to avoid chemicals in food, would support a sustainable aquaculture and present the advantage of being environmentally friendly. Using natural and renewable microalgal compounds is still in its early days, but considering the important research development and rapid improvement in culture, extraction and purification processes, the valorization of microalgae will surely extend in the future.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".