Polyherbal and Multimodal Treatments: Kaempferol- and Quercetin-Rich Herbs Alleviate Symptoms of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder impairing cognition and memory in the elderly. This disorder has a complex etiology, including senile plaque and neurofibrillary tangle formation, neuroinflammation, oxidative stress, and damaged neuroplasticity. Current treatment options are limited, so alternative treatments such as herbal medicine could suppress symptoms while slowing cognitive decline. We followed PRISMA guidelines to identify potential herbal treatments, their associated medicinal phytochemicals, and the potential mechanisms of these treatments. Common herbs, including Ginkgo biloba, Camellia sinensis, Glycyrrhiza uralensis, Cyperus rotundus, and Buplerum falcatum, produced promising pre-clinical results. These herbs are rich in kaempferol and quercetin, flavonoids with a polyphenolic structure that facilitate multiple mechanisms of action. These mechanisms include the inhibition of Aβ plaque formation, a reduction in tau hyperphosphorylation, the suppression of oxidative stress, and the modulation of BDNF and PI3K/AKT pathways. Using pre-clinical findings from quercetin research and the comparatively limited data on kaempferol, we proposed that kaempferol ameliorates the neuroinflammatory state, maintains proper cellular function, and restores pro-neuroplastic signaling. In this review, we discuss the anti-AD mechanisms of quercetin and kaempferol and their limitations, and we suggest a potential alternative treatment for AD. Our findings lead us to conclude that a polyherbal kaempferol- and quercetin-rich cocktail could treat AD-related brain damage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".