Amla enhances autophagy and modulates beta amyloid metabolism in an in vitro model of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a progressive, fatal neurodegenerative disease characterized by extensive neuronal loss associated with increased accumulation of the beta amyloid (Aβ) protein. Reducing production, preventing aggregation and improving clearance of Aβare areas of active research in the development of therapeutic agents to ameliorate neurodegeneration in AD. The Indian plant amla (Emblicaofficinalis), commonly known as Indian gooseberry, has widely been utilized in traditional Ayurvedic medicine preparations in the treatment of a variety of disease conditions including cardiovascular disease and diabetes: accumulating evidence also suggests that amla may be beneficial in AD. Amla exhibits antioxidant, anti-inflammatory, and anti-apoptotic mechanisms and more recently has been shown to modulate autophagy; a vital protein degradation pathway involved in the clearance of damaged organelles and aggregate proteins in cells. Our own recent in vitro work shows that amla extract enhances autophagy and modulates accumulation of proteolytic products of Amyloid precursor protein (APP) such as APP-C terminal fragments (C99, C83). Amla treatment (50-300 μg/ml) induced a dose-dependent increase in autophagic flux, as measured by Western blotting utilizing an LC3 directed antibody as an autophagosome marker. At similar concentrations, amla treatment also reduced accumulation of APP C-terminal fragment levels by 33 to 77%. However, no significant changes were observed in APP levels, indicating that amla did not alter APP production. Overall, our findings suggest that amla may confer beneficial effects through modulating autophagy and Aβ metabolism, and warrants further investigation as a potential therapeutic agent in AD
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".