CC Chemokine Receptor 2 Deficiency Aggravates Cognitive Impairments and Amyloid Pathology in a Transgenic Mouse Model of Alzheimer's Disease
Bibliographic record
Abstract
Circulating monocytoid cells have the ability to infiltrate nervous tissue, differentiate into microglia, and clear amyloid-β (Aβ) from the brain of mouse models of Alzheimer's disease. Interaction between the chemokine CCL2 and its CC chemokine receptor 2 (CCR2) plays a critical role in the recruitment of inflammatory monocytes into the injured/diseased brain. Here, we show that CCR2 deficiency aggravates mnesic deficits and amyloid pathology in transgenic mice expressing the chimeric mouse/human β-amyloid precursor protein and presenilin 1 (APP(Swe)/PS1). Indeed, memory impairment was accelerated and enhanced in APP(Swe)/PS1/CCR2(-/-) mice. Apparition of cognitive decline occurred earlier (i.e., at 3 months of age before plaque formation) and correlated with intracellular accumulation of soluble oligomeric forms of Aβ. Memory deficits worsened with age and were aggravated in APP(Swe)/PS1/CCR2(-/-) mice compared with their respective control groups. Soluble Aβ assemblies increased significantly in APP(Swe)/PS1 mice in a context of CCR2 deficiency, whereas the plaque load remained relatively similar in the brain of aging APP(Swe)/PS1 and APP(Swe)/PS1/CCR2(-/-) mice. However, CCR2 deficiency stimulated the expression of TGF-β1, TGF-β receptors, and CX(3)CR1 transcripts in plaque-associated microglia, a pattern that is characteristic of an antiinflammatory subset of myeloid cells. A decreased expression of CCR2 could play a potential role in the etiology of Alzheimer's disease, a neurodegenerative pathology that could be treated by a genetic upregulation of the transgene in monocytoid cells.
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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 | 0.000 |
| 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".