Anti-Amyloidogenic Effect of Allium sativum in Alzheimer's Transgenic Model Tg2576
Bibliographic record
Abstract
High levels of cholesterol are implicated in potentiating Alzheimer's disease (AD). Therefore, the use of cholesterol-lowering agents such as statins has attracted considerable interest in treating AD. However, statins stimulate inflammatory response, which may aggravate AD-pathology. Although garlic (Allium sativum) is historically known for its hypocholesterolemic effects in relation to cardiovascular functions, no reports indicate its use in treating AD. Current study tested the feasibility of using dietary garlic on the reduction of amyloid burden in a transgenic mouse model of AD that overexpresses the human amyloid precursor protein 695 carrying Swedish double mutation (K670N/M671L) (Tg2576). Animals were treated with aged garlic extract (40 mg/kg/d/4 wks). Cerebral levels of sAPPa, sAβ40, sAβ42 were analyzed by sandwich ELISA. Results show 64% reduction of sAPPa, and ∼21-fold elevation of Aβ40 and Aβ42 in untreated Tgs compared to wild type and littermate controls. Dietary garlic increased sAPPa by 25% and de- creased Aβ40 and Aβ42 by 31% and 32%, respectively, compared to untreated Tgs. These results suggest a simple and non-invasive dietary therapy for reducing risk of AD in probable cases and reducing preexisting amyloid burden in clinically diagnosed AD cases.
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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 | 0.000 |
| 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) | 0.002 | 0.000 |
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".