Prior activation state shapes the microglia response to antihuman TREM2 in a mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Significance Alzheimer’s disease (AD) is the most common dementia; no therapy halts its progression. AD pathology, including amyloid-β (Aβ) plaques, neurofibrillary tangles, and synapse loss, triggers microglial responses that modulate disease course. The TREM2 receptor promotes microglia responses to pathology and a variant, TREM2 R47H , impairs ligand binding and increases AD risk. Employing scRNA-seq, we asked: can an anti-TREM2 antibody, acting as a surrogate ligand, stimulate microglia in mice that accumulate Aβ and express either the common TREM2 variant ( TREM2 CV ) or TREM2 R47H ? One systemic injection of anti-TREM2 restored microglia activation in TREM2 R47H mice but promoted limited activation in mice carrying TREM2 CV , which binds endogenous ligands. Thus, anti-TREM2 can strengthen microglial responses during AD, contingent on preexisting TREM2 engagement and basal activation.
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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 | 0.000 | 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".