Dietary supplementation with <i>Allium hirtifolium</i> and/or <i>Astragalus hamosus</i> improved memory and reduced neuro-inflammation in the rat model of Alzheimer’s disease
Bibliographic record
Abstract
Allium hirtifolium Boiss and Astragalus hamosus L. are mentioned in Iranian traditional medicine documentation as therapy for a kind of dementia with the features and symptoms similar to those of Alzheimer’s disease (AD). In the present study, the effects of these herbs on neuro-inflammation and memory have been evaluated as new therapies in amyloid beta (Aβ)-induced memory impairment model. Separate groups of rats were fed with A. hirtifolium or A. hamosus extract (both 100 mg/(kg·day)−1) started 1 week before stereotaxic surgery to 24 h before behavioral testing (totally, for 16 successive days). The effects of oral administration of mentioned extracts on the memory and neuro-inflammation were assessed in the Aβ-injected rats. The results of this study showed that oral administration of both A. hirtifolium and A. hamosus improved the memory, examined by using Y-maze test and shuttle box apparatus. Also, Western blotting analysis of cyclooxygenase-2, interleukin-1β, and tumor necrosis factor-α showed that these herbs have ameliorating effects against the neuro-inflammation caused by Aβ. These findings suggest that the use of A. hirtifolium and A. hamosus as herbal therapy may be suitable for decreasing AD-related symptoms and treatment of other neurodegenerative disorders.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".