Microglial senescence in neurodegeneration: Insights, implications, and therapeutic opportunities
Bibliographic record
Abstract
The existing literature on neurodegenerative diseases (NDDs) reveals a common pathological feature: the accumulation of misfolded proteins. However, the heterogeneity in disease onset mechanisms and the specific brain regions affected complicates the understanding of the diverse clinical manifestations of individual NDDs. Dementia, a hallmark symptom across various NDDs, serves as a multifaceted denominator, contributing to the clinical manifestations of these disorders. There is a compelling hypothesis that therapeutic strategies capable of mitigating misfolded protein accumulation and disrupting ongoing pathogenic processes may slow or even halt disease progression. Recent research has linked disease-associated microglia to their transition into a senescent state-characterized by irreversible cell cycle arrest-in aging populations and NDDs. Although senescent microglia are consistently observed in NDDs, few studies have utilized animal models to explore their role in disease pathology. Emerging evidence from experimental rat models suggests that disease-associated microglia exhibit characteristics of senescence, indicating that deeper exploration of microglial senescence could enhance our understanding of NDD pathogenesis and reveal novel therapeutic targets. This review underscores the importance of investigating microglial senescence and its potential contributions to the pathophysiology of NDDs, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Additionally, it highlights the potential of targeting microglial senescence through iron chelation and senolytic therapies as innovative approaches for treating age-related NDDs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".