A Comprehensive Review of Fortification, Bioavailability, and Health Benefits of Folate
Bibliographic record
Abstract
Folate is an essential vitamin involved in one-carbon metabolism. It can be acquired from many food sources or in synthetic form. A wide range of processing methods have been studied to improve the bioaccessibility and bioavailability of folate in foods, yet this is often accompanied by a decrease in stability. Encapsulation technology has emerged as an effective solution for protecting folate from degradation and liberation while also improving its bioavailability. Folate deficiency is a prevalent phenomenon worldwide, particularly in underprivileged countries, leading to various health problems, such as neural tube defects. Thus, folate was fortified through both exogenous addition and biofortification. Gene editing technology, especially CRISPR-Cas9, has great promise in this field when compared to transgenic engineering, because transgenic engineering may pose safety concerns and environmental risks. While ongoing research has identified additional potential effects of folate, the dosage and duration remain important factors to consider for optimal health outcomes. The mechanisms of how folate promotes the production of neurotransmitters associated with the gut microbiota-brain axis and reduces depression are not well understood. In addition to folate alone, there may be synergistic effects of combined supplementation of folate and other nutrients or medications, but this is not yet fully clarified and requires further examination. This review summarizes the food sources, enrichment, bioaccessibility, and bioavailability of folate. Furthermore, the health benefits of folate, including neural tube protection, cardiovascular protection, neuroprotection, anti-cancer, immune response augmentation, and gut homeostasis maintenance, with their potential bioactivity mechanisms, are discussed.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".