Chimeric Antigen Receptor Macrophages Target and Resorb Amyloid Plaques in a Mouse Model of Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Substantial evidence suggests a role for immunotherapy in treating Alzheimer’s disease (AD). Several monoclonal antibodies targeting aggregated forms of beta amyloid (Aβ), have been shown to reduce amyloid plaques and in some cases, mitigate cognitive decline in early-stage AD patients. We sought to determine if genetically engineered macrophages could improve the targeting and degradation of amyloid plaques. Chimeric antigen receptor macrophages (CAR-Ms), which show promise as a cancer treatment, are an appealing strategy to enhance target recognition and phagocytosis of amyloid plaques in AD. We genetically engineered macrophages to express a CAR containing the anti-amyloid antibody aducanumab as the external domain and the Fc receptor signaling domain internally. CAR-Ms recognize and degrade Aβ in vitro and on APP/PS1 brain slices ex vivo; however, when injected intrahippocampally, these first-generation CAR-Ms have limited persistence and fail to reduce plaque load. We overcame this limitation by creating CAR-Ms that secrete M-CSF and self-maintain without exogenous cytokines. These CAR-Ms have greater survival in the brain niche, and significantly reduce plaque load locally in vivo . These proof-of-principle studies demonstrate that CAR-Ms, previously only applied to cancer, may be utilized to target and degrade unwanted materials, such as amyloid plaques in the brains of AD mice. Amyloid targeting CAR Macrophages engineered to secrete M-CSF promote their own local survival and expansion while resorbing amyloid plaques in the brains of Alzheimer’s disease APP/PS1 mice, resulting in significant local clearance of amyloid plaques of all sizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".