The role of neuroinflammation and amyloid in cognitive impairment in an <scp>APP</scp>/<scp>PS</scp>1 transgenic mouse model of Alzheimer's disease
Bibliographic record
Abstract
AIMS: Both amyloid deposition and neuroinflammation appear in the early course of Alzheimer's disease (AD). However, the progression of neuroinflammation and its relationship with amyloid deposition and behavioral changes have not been fully elucidated. A better understanding the role of neuroinflammation in AD might extend our current knowledge to therapeutic intervention possibilities. METHODS: This study systematically characterized changes in behavioral abnormalities in APP/PS1 transgenic mice. Brain pathology measures were performed in post-mortem brain tissues of mice from 2 to 22 months. RESULTS: APP/PS1 mice exhibited significant memory deficits from 5 months old, which were aggravated at the later stage of life. However, the degree of memory impairments reached a plateau at 12 months. An early appearance of amyloid plaques was at 3 months with a linear increase throughout the disease course. CD11b-positive microglia and glial fibrillary acidic protein-(GFAP) positive astrocytes were first detected at 3 months with a close association with amyloid plaques. Yet, the rate of changes in glial activation slowed down from 12 months despite the steady increase in Aβ. CONCLUSION: These findings provided evidence that neuroinflammation might be involved in the development and progression of cognitive deficits in APP/PS1 mice, suggesting novel intervention and prevention strategies for AD.
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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) | 0.000 | 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 | 0.000 | 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".