Targeted Antioxidant, Catalase–SKL, Reduces Beta‐Amyloid Toxicity in the Rat Brain
Bibliographic record
Abstract
Abstract Accumulation of beta‐amyloid (Aβ) in the brain has been implicated as a major contributor to the cellular pathology and cognitive impairment observed in Alzheimer's disease. Beta‐amyloid may exert its toxic effects by increasing reactive oxygen species and neuroinflammation in the brain. This study set out to investigate whether a genetically engineered derivative of the peroxisomal antioxidant enzyme catalase (CAT–SKL), is able to reduce the toxicity induced by intracerebroventricular injection of Aβ25–35 in the mature rat brain. Histopathological and immunohistochemical analyses were used to evaluate neuroinflammation, and neuronal loss. Spatial learning and reference memory was assessed using the Morris water maze. CAT–SKL treatment was able to reduce the pathology induced by Aβ25‐35 toxicity by significantly decreasing microglia activation in the basal forebrain and thalamus, and reducing cholinergic loss in the basal forebrain. Aβ25–35 animals showed deficits in long‐term reference memory in the Morris water maze, while Aβ25–35 animals treated with CAT–SKL did not demonstrate long‐term memory impairments. This preclinical data provides support for the use of CAT–SKL in reducing neuroinflammation and long‐term reference memory deficits induced by Aβ25–35.
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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) | 0.000 | 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) | 0.001 | 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".