Infiltrating Hematogenous Macrophages Aggregate Around β-Amyloid Plaques in an Age- and Sex-Dependent Manner in a Mouse Model of Alzheimer Disease
Bibliographic record
Abstract
β-Amyloid (Aβ) plaques can trigger chronic inflammation in the cellular environment that recruits infiltrating macrophages during the course of Alzheimer disease (AD). Activated macrophages release pro-inflammatory cytokines that increase neurotoxicity associated with AD. A major impediment to investigating neuroinflammation involving macrophage activity is the inability to discriminate resident microglial macrophages (mMϕ) from hematogenous macrophages (hMϕ), as they are morphologically and phenotypically similar when activated. To distinguish between mMϕ and hMϕ and to determine their respective roles in chronic inflammation associated with the progression of amyloidosis, we used lys-EGFP-ki transgenic mice that express enhanced green fluorescent protein in hMϕ, but not in mMϕ. These mice were crossed with 5XFAD mice. The offspring demonstrated robust AD pathology and enabled visual discrimination of mMϕ from hMϕ. Mutant mice demonstrated robust increases in Aβ1-42, area of Aβ plaques, gliosis and deficits in spatial learning by age 5 months. The time-course of Aβ accumulation, paralleled by the accumulation of hMϕ around Aβ plaques, was more robust in female compared with male mice and preceded behavioral changes. Thus, the accumulation of infiltrating hMϕ around Aβ plaques was age- and sex-dependent and preceded cognitive impairment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".