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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it