Early-Stage Inflammation and Experimental Therapy in Transgenic Models of the Alzheimer-Like Amyloid Pathology
Bibliographic record
Abstract
BACKGROUND: Intracellular accumulation of beta-amyloid (Abeta) is one of the early features in the neuropathology of Alzheimer's disease (AD) and Down's syndrome. This can be reproduced in cell and transgenic animal models of the AD-like amyloid pathology. In a transgenic rat model, our lab has previously shown that the intracellular accumulation of Abeta is sufficient to provoke cognitive impairments and biochemical alterations in the cerebral cortex and hippocampus in the absence of amyloid plaques. OBJECTIVE: To investigate an early, pre-plaque inflammatory process in AD-like transgenic models and establish whether the neurotoxic effects of Abeta oligomers and proinflammatory responses can be arrested with minocycline. METHODS: For these studies, we used naïve mice and transgenic animal models of the AD-like amyloid pathology and applied neurochemical, immunohistochemical and behavioral experimental approaches. RESULTS: In the early stages of the AD-like amyloid pathology, intracellular Abeta oligomers accumulate within neurons of the cerebral cortex and hippocampus. Coincidental with this, behavioral impairments occur prior to the appearance of amyloid plaques, together with an upregulation of MHC-II, i-NOS and COX-2, well-known proinflammatory markers. Treatment with minocycline corrected behavioral impairments, lowered inflammatory markers and levels of Abeta trimers. CONCLUSION: A pharmacological approach targeting the early neuroinflammatory effects of Abeta might be a promising strategy to prevent or delay the onset of 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".