Progressive Increase in Infarct Size, Neuroinflammation, and Cognitive Deficits in the Presence of High Levels of Amyloid
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In the elderly, cerebral ischemia (CI) occurs in the presence of high levels of amyloid. Neuroinflammation plays a critical role in the pathophysiology of Alzheimer's disease and CI. This study examined infarct size, neuroinflammation, and cognitive deficits over time in rat models of Alzheimer's disease and CI. METHODS: beta-amyloid toxicity was modeled using bilateral intracerebroventricular injections of beta-amyloid 25 to 35 peptides. CI was modeled using unilateral injections of the potent vasoconstrictor, endothelin-1, into the striatum. RESULTS: Infarct volumes were higher in the presence of amyloid and compared with the CI model alone. In the CI model alone, the infarct volume was significantly smaller 28 days after surgery compared with 7 days after surgery. However, when Alzheimer's disease and CI models were combined, the infarct volume was significantly larger 28 days after surgery compared with 7 days after surgery. The neuroinflammation in the region of the infarct was also significantly increased. The Barnes circular platform test showed time-dependent increases in memory and learning deficits in the beta-amyloid-treated rats that were even greater when beta-amyloid treatment was combined with CI. CONCLUSIONS: CI in the presence of high levels of amyloid results in progressive increases in infarct size, neuroinflammation, and cognitive deficits.
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 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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".