Phytochemical Interactions with Calmodulin and Critical Calmodulin Binding Proteins Involved in Amyloidogenesis in Alzheimer’s Disease
Bibliographic record
Abstract
An increasing number of plant-based herbal treatments, dietary supplements, medical foods and nutraceuticals and their component phytochemicals are used as alternative treatments to prevent or slow the onset and progression of Alzheimer's disease. Their appeal stems from the fact that no current pharmaceutical or medical treatment can accomplish this. While a handful of pharmaceuticals are approved to treat Alzheimer's, none has been shown to prevent, significantly slow or stop the disease. As a result, many see the appeal of alternative plant-based treatments as an option. Here, we show that many phytochemicals proposed or used as Alzheimer's treatments share a common theme: they work via a calmodulin-mediated mode of action. Some phytochemicals bind to and inhibit calmodulin directly while others bind to and regulate calmodulin-binding proteins, including Aβ monomers and BACE1. Phytochemical binding to Aβ monomers can prevent the formation of Aβ oligomers. A limited number of phytochemicals are also known to stimulate calmodulin gene expression. The significance of these interactions to amyloidogenesis in Alzheimer's disease is reviewed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".