Deciphering mechanisms of action of ACE inhibitors in neurodegeneration using Drosophila models of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a devastating neurodegenerative disorder for which there is no cure. Recently, several studies have reported a significant reduction in the incidence and progression of dementia among some patients receiving antihypertensive medications such as angiotensin-converting enzyme inhibitors (ACE-Is) and angiotensin receptor blockers (ARBs). Why these drugs are beneficial in some AD patients and not others is unclear although it has been shown to be independent of their role in regulating blood pressure. Given the enormous and immediate potential of ACE-Is and ARBs for AD therapeutics it is imperative that we understand how they function. Recently, studies have shown that ACE-Is and ARBs, which target the renin angiotensin system in mammals, are also effective in suppressing neuronal cell death and memory defects in Drosophila models of AD despite the fact that this pathway is not conserved in flies. This suggests that the beneficial effects of these drugs may be mediated by distinct and as yet, identified mechanisms. Here, we discuss how the short lifespan and ease of genetic manipulations available in Drosophila provide us with a unique and unparalleled opportunity to rapidly identify the targets of ACE-Is and ARBs and evaluate their therapeutic effectiveness in robust models of 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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".