Nootropic foods in neurodegenerative diseases: mechanisms, challenges, and future
Bibliographic record
Abstract
Neurodegenerative diseases (NDDs) such as Alzheimer's and Parkinson's disease are increasing globally and represent a significant cause of age-related death in the population. Recent studies emphasize the strong association between environmental stressors, particularly dietary factors, and brain health and neurodegeneration unsatisfactory outcomes. Despite ongoing efforts, the efficiency of current treatments for NDDs remains wanting. Considering this, nootropic foods with neuroprotective effects are of high interest as part of a possible long-term therapeutic strategy to improve brain health and alleviate NDDs. However, since it is a new and emerging area in food and neuroscience, there is limited information on mechanisms and challenges to consider for this to be a successful intervention. Here, we seek to address these gaps by presenting a comprehensive review of possible pathways or mechanisms including mutual interactions governing nootropic food metabolism, linkages of the pathways with NDDs, intake, and neuroprotective properties of nootropic foods. We also discuss in-depth intervention with nootropic compounds and dietary patterns in NDDs, providing a detailed exploration of their mechanisms of action. Additionally, we analyze the demand, challenges, and future directions for successful development of nootropic foods targeting NDDs.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".