Advances in selenium supplementation: From selenium-enriched yeast to potential selenium-enriched insects, and selenium nanoparticles
Bibliographic record
Abstract
Selenium (Se) is an essential micronutrient that plays an important role in animal and human development and physiological homoeostasis. This review surveys the role of Se in the environment, plants and animal bodies, and discusses data on Se biofortification with different sources of supplementation, from inorganic to organic forms, with special focus on Se-enriched yeast (Se-yeast). Although Se-yeast remains one of the main sources of organic Se, other emerging and innovative sources are reviewed, such as Se-enriched insects and Se-nanoparticles and their potential use in animal nutrition. Se-enriched insects are discussed as an option for supplying Se in organic form to livestock diets. Se-nanoparticles are also discussed, as they represent a more biocompatible and less toxic source of inorganic Se for animal organisms, compared to selenite and selenate. We also provide up to date information on the legal framework in the EU, USA, and Canada of Se that is contained in feed additives. From the scientific evidence available in the literature, it can be concluded that among the inorganic forms, sodium selenite is still one of the main options, whereas Se-yeast remains the primary organic form. However, other potential sources such as Se-enriched insects and Se-nanoparticles are being investigated as they could potentially combine a high bioavailability and reduced Se emissions in the environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".