Bioactives in Food-As-Medicine for Special Medical Purposes
Bibliographic record
Abstract
With rising global attention to health and the accelerating trend of population aging, the demand for Foods for Special Medical Purposes (FSMPs) has increased substantially. These products are formulated not only to meet the basic nutritional needs of individuals with specific diseases or physiological conditions but also to provide regulatory physiological effects. In this context, the "Food-As-Medicine" (FAM) concept has gained growing interest. FAM seeks to prevent and treat diseases through the incorporation of functional foods into health management strategies and aligns closely with the traditional Chinese medicine (TCM) theory of "Food-Medicine Homologous" (FMH). Guided by FMH principles, natural bioactive compounds such as polysaccharides, flavonoids, and saponins have drawn significant attention for their anti-inflammatory, antioxidant, and immunomodulatory properties. However, their broader application faces several challenges, including low extraction efficiency, complex purification procedures, and difficulties in ensuring stability, all of which hinder industrial-scale development. This review systematically explores the potential, challenges, and opportunities of FMH-based products in FSMPs, with a particular emphasis on infant nutrition, maternal health, and the management of chronic diseases in the elderly. Our findings highlight the dual value of FMH ingredients in both nutritional support and functional modulation. Furthermore, by integrating FMH theory with modern nutritional science, this review offers a scientific basis for the innovative development of FSMPs. Given the growing global market demand and the increasing dissemination of TCM culture, this field is poised to enter a new phase of development.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".