DIETARY POLYPHENOLS AND THEIR GUT MICROBIOTA METABOLITES AS POTENTIAL TREATMENTS FOR ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that has no cure, no therapies to stop the disease progression, and limited palliative care options. As the 6th leading cause of death in the United States, it is necessary to discover therapeutics which can alter disease progression to alleviate the burden on patients, caregivers, and healthcare systems. In the search of therapeutics for AD, dietary polyphenols have become the topic of research as epidemiological evidence suggests that exposure to dietary polyphenols, such as a Mediterranean diet, over one’s life, reduces the risk of dying from AD. Additionally, evidence from rodent models of AD suggests that dietary polyphenols can reduce the severity of cognitive decline and attenuate neuroinflammation, which is thought to be a contributing factor to disease progression. Because of these findings, several dietary polyphenols have entered the clinic in an attempt to elucidate their potential beneficial effects. For example, curcumin, from the curry spice turmeric (Curcuma longa), and resveratrol, a phytoalexin stilbene found in a variety of plants, have been investigated in AD disease populations in phase I and II clinical studies. However, these agents have only had moderate to little success which increases the demand for novel dietary polyphenols leads that may have potentially beneficial effects in the treatment of AD. Due to this demand, this dissertation investigates novel polyphenols and gut metabolites of dietary polyphenols from a variety of dietary sources for potential efficacy in a Caenorhabditis elegans (C. elegans) model of AD. This dissertation also employs molecular biology and mass spectrometry techniques to elucidate the potential mechanisms of action of select compounds found to be efficacious in a C. elegans model of AD. Finally, a polyphenol-enriched extract was evaluated in a murine model of AD for the potential to reduce neuroinflammation when administered orally.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".