Health Benefits, Food Applications, and Sustainability of Microalgae-Derived N-3 PUFA
Bibliographic record
Abstract
Today's consumers are increasingly aware of the beneficial effects of n-3 PUFA in preventing, delaying, and intervening various diseases, such as coronary artery disease, hypertension, diabetes, inflammatory and autoimmune disorders, neurodegenerative diseases, depression, and many other ailments. The role of n-3 PUFA on aging and cognitive function is also one of the hot topics in basic research, product development, and clinical applications. For decades, n-3 PUFA, especially EPA and DHA, have been supplied by fish oil and seafood. With the continuous increase of global population, awareness about the health benefits of n-3 PUFA, and socioeconomic improvement worldwide, the supply chain is facing increasing challenges of insufficient production. In this regard, microalgae have been well considered as promising sources of n-3 PUFA oil to mitigate the supply shortages. The use of microalgae to produce n-3 PUFA-rich oils has been explored for over two decades and some species have already been used commercially to produce n-3 PUFA, in particular EPA- and/or DHA-rich oils. In addition to n-3 PUFA, microalgae biomass contains many other high value biomolecules, which can be used in food, dietary supplement, pharmaceutical ingredient, and feedstock. The present review covers the health benefits of n-3 PUFA, EPA, and DHA, with particular attention given to the various approaches attempted in the nutritional interventions using EPA and DHA alone or combined with other nutrients and bioactive compounds towards improved health conditions in people with mild cognitive impairment and Alzheimer's disease. It also covers the applications of microalgae n-3 PUFA in food and dietary supplement sectors and the economic and environmental sustainability of using microalgae as a platform for n-3 PUFA-rich oil production.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".