Microglial Inflammation and Cognitive Dysfunction in Comorbid Rat Models of Striatal Ischemic Stroke and Alzheimer’s Disease: Effects of Antioxidant Catalase-SKL on Behavioral and Cellular Pathology
Bibliographic record
Abstract
Ischemic stroke often co-occurs with Alzheimer’s disease (AD) leading to a worsened clinical outcome. Neuroinflammation is a critical process implicated in AD and ischemic pathology, associated with cognitive decline. We sought to investigate the combined effects of ischemic stroke induced by endothelin-1 injection in two AD rat models, using motor function, memory and microglial inflammation in the basal forebrain and striatum as readouts. In addition, we sought to determine the effectiveness of the antioxidant biologic CAT-SKL in one of the models. The early AD model employed the bilateral intracerebroventricular injections of the toxic β-amyloid peptide Aβ25–35, the prodromal AD model used the transgenic Fischer 344 rat overexpressing a pathological mutant human amyloid precursor protein. Motor function was assessed using a cylinder, modified sticky tape and beam-walk tasks; learning and memory were tested in the Morris water maze. Microglial activation was examined using immunohistochemistry. Aβ25–35 toxicity and stroke combination greatly increased microglial inflammation in the basal forebrain. Prodromal AD-pathology coupled with ischemia in the transgenic rat resulted in a greater microgliosis in the striatum. Combined transgenic rats showed balance alterations, comorbid Aβ25–35 rats showed a transient sensorimotor deficit, and both demonstrated spatial reference memory deficit. CAT-SKL treatment ameliorated memory impairment and basal forebrain microgliosis in Aβ25–35 rats with stroke. Our results suggest that neuroinflammation could be one of the early processes underlying the interaction of AD with stroke and contributing to the cognitive impairment, and that therapies such as antioxidant CAT-SKL could be a potential therapeutic strategy.
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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 | 0.001 |
| 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 | 0.001 | 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".