Food‐derived Acetylcholinesterase Inhibitors as Potential Agents against Alzheimer’s Disease
Bibliographic record
Abstract
Acetylcholinesterase (AChE) is a critical enzyme involved in nerve functions and signal transmission within the brain. However, during aging, excessive AChE activity often leads to rapid and progressive depletion of acetylcholine (ACh), the major neurotransmitter. Shortage of ACh leads to reduced neurotransmission and the development of pathological conditions such as dementia and Alzheimer’s Disease (AD). Therefore, one of the proven approaches toward the clinical management of AD is the use of compounds that inhibit AChE activity to produce enhanced brain levels of ACh and restore regular nerve functions in the brain. Hence the aim of this review is to provide information on recent advances in the use of various food‐derived extracts and compounds as AChE‐inhibitory agents. The major forms of AChE‐inhibitory products are the aqueous or organic solvent extracts of various foods with polyphenolic compounds being the predominant constituents. Other types of food‐derived AChE inhibitors include proteins, peptides, terpenoids and carotenoids. In addition to the proven efficacy at the in vitro level, several of these food products have been shown to be effective in reducing brain levels of AChE with concomitant improvements in memory functions. However, future research activities are needed to provide information on the structure‐function and toxicological aspects of food‐derived AChE‐inhibitory compounds.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".