Evaluating ambroxol as an agent to boost lysosomal function and reduce beta amyloid in 3xTg mice
Bibliographic record
Abstract
BACKGROUND: Ambroxol is an expectorant under study as a treatment for synucleinopathies, such as Parkinson's disease. It is a pharmacological chaperone of the lysosomal enzyme β-glucocerebrosidase (GCase), increasing this enzyme and subsequently reducing accumulation of alpha-synuclein. Although the mechanism of enhanced clearance is not fully understood, ambroxol stimulates lysosomal function through activation of transcription factor EB (TFEB), which drives hundreds of lysosomal genes. This project aims to investigate the usefulness of ambroxol as a disease-modifying treatment for Alzheimer's disease (AD). We hypothesize that ambroxol will enhance expression of lysosomal proteins and increase clearance of beta-amyloid (Aβ) in the 3xTg mouse model of AD. METHODS: Wild-type (B6;129) or 3xTg mice (APP/PS1/MAPT) mice were used. A 'prevention' group was treated at 6 months of age for 2 months, during initial Aβ deposition. A second 'treatment group' was fed ambroxol from 8 to 10 months of age, after Aβ deposits had developed. Mice received either 1) normal mouse chow, 2) low dose (1200 mg/kg) chow, or 3) or high dose (2400 mg/kg) chow. Brains were collected for immunohistochemistry, western blotting and ELISAs. RESULTS: By immunostaining, mean expression of ambroxol treated animals demonstrated increased GCase and LAMP1 staining, as predicted. This was accompanied by a significant dose dependant reduction of Aβ in aged 3xTg mice. CONCLUSIONS: Our findings demonstrate that ambroxol is able to reduce intracellular accumulation of Aβ by upregulating lysosomal activity, and therefore may be a promising treatment for AD.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".