Food Bioactives: Impact on Brain and Cardiometabolic Health – Findings from In Vitro to Human Studies
Bibliographic record
Abstract
The search for dietary patterns or food bioactive derivatives that may serve as a panacea for health issues has been a topic of interest for several millennia. It is not surprising that this trend in food research is continuing today particularly in relation to brain and cardiometabolic health, given the huge burden they pose on human health, with no geographical boundaries. Currently, there is an increasing demand for ‘pure’ and ‘clean’ foods as well as potent bioactive ingredients that can promote beneficial health outcomes. Several studies, including in vitro investigations, clinical trials, and observational studies related to food and nutritional patterns have already identified, proposed, and in some cases challenged the mechanisms of action of these foods and food ingredients. The book “Food bioactives and impact on brain and cardiometabolic health findings from in vitro to human studies” has gathered innovative, high-quality research manuscripts (letters to the editor, original research and review papers) on bioactive constituents of foods and dietary patterns which can directly impact upon brain and cardiometabolic health. These manuscripts reporting on different areas of this research field, from the description of new conceptual ideas, mechanisms of action, and structural modelling to clinical trials and observational studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".