The Specific Mechanism of TREM2 Regulation of Synaptic Clearance in Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease. Synaptic dysfunction is an integral feature of AD pathophysiology and a significant factor in early cognitive impairment in AD. Microglia, which are intrinsic immune cells in the central nervous system, play important regulatory roles in the process of synapse formation. Microglia can refine synaptic connections through synaptic clearance to ensure accurate synaptic transmission. Synaptic clearance is not only existed during central nervous system development but also aberrantly activated during AD pathology. However, the mechanisms of synaptic clearance in AD remain to be investigated. TREM2 is involved in the synaptic clearance of microglia, acting alone or with other molecules, such as apolipoprotein E (APOE). In addition, C1q is essential for microglia-mediated synaptic clearance. In this review, we systematically summarized the potential mechanisms of microglia involved in synaptic clearance, comprehensively reviewed the role of TREM2 in microglia regulating synaptic clearance and proposed our hypothesis that TREM2 interacts with APOE and C1q to promote synaptic clearance. This review provides new insights into the role of TREM2 regulation in microglia synaptic clearance and provides potential prospects for the treatment of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".