Death-associated protein kinase 1 as a therapeutic target for Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most prevalent form of dementia in the elderly and represents a major clinical challenge in the ageing society. Neuropathological hallmarks of AD include neurofibrillary tangles composed of hyperphosphorylated tau, senile plaques derived from the deposition of amyloid-β (Aβ) peptides, brain atrophy induced by neuronal loss, and synaptic dysfunctions. Death-associated protein kinase 1 (DAPK1) is ubiquitously expressed in the central nervous system. Dysregulation of DAPK1 has been shown to contribute to various neurological diseases including AD, ischemic stroke and Parkinson's disease (PD). We have established an upstream effect of DAPK1 on Aβ and tau pathologies and neuronal apoptosis through kinase-mediated protein phosphorylation, supporting a causal role of DAPK1 in the pathophysiology of AD. In this review, we summarize current knowledge about how DAPK1 is involved in various AD pathological changes including tau hyperphosphorylation, Aβ deposition, neuronal cell death and synaptic degeneration. The underlying molecular mechanisms of DAPK1 dysregulation in AD are discussed. We also review the recent progress regarding the development of novel DAPK1 modulators and their potential applications in AD intervention. These findings substantiate DAPK1 as a novel therapeutic target for the development of multifunctional disease-modifying treatments for AD and other neurological disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".