Enhancing autophagy in Alzheimer's disease through drug repositioning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alzheimer's disease (AD) is one of the biggest human health threats due to increases in aging of the global population. Unfortunately, drugs for treating AD have been largely ineffective. Interestingly, downregulation of macroautophagy (autophagy) plays an essential role in AD pathogenesis. Therefore, targeting autophagy has drawn considerable attention as a therapeutic approach for the treatment of AD. However, developing new therapeutics is time-consuming and requires huge investments. One of the strategies currently under consideration for many diseases is "drug repositioning" or "drug repurposing". In this comprehensive review, we have provided an overview of the impact of autophagy on AD pathophysiology, reviewed the therapeutics that upregulate autophagy and are currently used in the treatment of other diseases, including cancers, and evaluated their repurposing as a possible treatment option for AD. In addition, we discussed the potential of applying nano-drug delivery to neurodegenerative diseases, such as AD, to overcome the challenge of crossing the blood brain barrier and specifically target molecules/pathways of interest with minimal side effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it