Improving effects of Echium amoenum aqueous extract on rat model of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder greatly accompanied by oxidative stress and acetylcholine reduction in synaptic cleft that leads to dementia. Previously approved there is correlation between nucleus basalis of Meynert (NBM) degeneration and loss of memory, learning ability and thought. The aim of this study was to investigate improving effects of Echium amoenum aqueous extract on memory deficient, pathophysiological and oxidative damages imposed by NBM lesion in rats as documented AD model. Results showed NBM destruction causes hash oxidative stress that possibly leads to neurodegeneration in hippocampus tissue. Orally administration of plant extract significantly reduced oxidative stress by reactive molecules scavenging that resulted to decrease lipid peroxidation also. Plant extract treatment inhibited acetylcholine esterase enzyme (more than 5 folds) in hippocampus tissue related to NBM lesioned rats. Histological studies approved NBM lesion causes harsh neurodegeneration in hippocampus tissue possibly by acetylcholine reduction that was compensated by plant extract protective effects. Interestingly improving effects of plant in molecular level causes improved spatial learning ability in Morris water maze test. By considering pathophysiological and molecular similarities between AD and NBM lesion model, E. amoenum could be used as a therapeutic adjuvant in patients suffering from Alzheimer or similar cognitive 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) | 0.001 | 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) | 0.001 | 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".