Molecular Signaling Pathways in Alzheimer’s Disease and Their Therapeutic Implications
Bibliographic record
Abstract
Alzheimer’s disease (AD) poses significant health challenges for the elderly due to its complex origins and heterogeneous etiology. Currently, there are no medications specifically aimed at halting its progression through any preventive signaling mechanism. Therefore, there is a pressing need to explore alternative pathways that could guide researchers towards new avenues in drug development for managing AD sustainability. This article is the first to simultaneously review several potential signaling pathways that were previously reported as individual signaling conduits in AD. Through meticulous analysis, we assess the pathophysiological roles of these pathways in AD. Extensive literature searches across databases including PubMed, Cochrane Library, Web of Science, Scopus, ResearchGate, Google Scholar, X-Mol, EBSCO, Loop, and Google, encompassing both research articles and highly cited reviews, were conducted. In addition to the cholinergic and N-methyl D-aspartate (NMDA) glutamate signaling pathways, we have observed four other less-explored, but promising molecular pathways relevant to AD onset and progression. We delved into the detailed signaling mechanisms of these pathways, spanning from preclinical to clinical studies. Remarkably, each pathway demonstrates distinct molecular signaling patterns and offers diverse perspectives on AD treatment. This review highlights six pivotal signaling pathways that may hold promise as future targeting pathways in managing 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".