Powerful beneficial effects of macrophage colony-stimulating factor on -amyloid deposition and cognitive impairment in Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease is a major cause of dementia in humans. The appearance of cognitive decline is linked to the overproduction of a short peptide called beta-amyloid (Abeta) in both soluble and aggregate forms. Here, we show that injecting macrophage colony-stimulating factor (M-CSF) to Swedish beta-amyloid precursor protein (APP(Swe))/PS1 transgenic mice, a well-documented model for Alzheimer's disease, on a weekly basis prior to the appearance of learning and memory deficits prevented cognitive loss. M-CSF also increased the number of microglia in the parenchyma and decreased the number of Abeta deposits. Senile plaques were smaller and less dense in the brain of M-CSF-treated mice compared to littermate controls treated with vehicle solution. Interestingly, a higher ratio of microglia internalized Abeta in the brain of M-CSF-treated animals and the phagocytosed peptides were located in the late endosomes and lysosomes. Less Abeta(40) and Abeta(42) monomers were also detected in the extracellular protein enriched fractions of M-CSF-treated transgenic mice when compared with vehicle controls. Finally, treating APP(Swe)/PS1 mice that were already demonstrating installed Abeta pathology stabilized the cognitive decline. Together these results provide compelling evidence that systemic M-CSF administration is a powerful treatment to stimulate bone marrow-derived microglia, degrade Abeta and prevent or improve the cognitive decline associated with Abeta burden in a mouse model of Alzheimer's disease.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".