Close Association of Myeloperoxidase-Producing Activated Microglia with Amyloid Plaques in Hypercholesterolemic Rabbits
Bibliographic record
Abstract
Microglial activation and oxidative stress have been linked to the formation of amyloid plaques found in Alzheimer's disease (AD). Epidemiologic and experimental evidence also suggests that cholesterol (CH) contributes to the pathogenesis of AD, particularly the formation of amyloid plaques. We have previously described the development of amyloid-β (Aβ) plaques in New Zealand white rabbits maintained on a 0.125%-0.25% w/w CH diet for extended periods of time (28 months). Here we further characterize this model with combined immunofluorescence and immunohistochemical staining to evaluate markers of immune cell activation. Five out of eight CH-fed rabbits, but not control rabbits, developed extracellular Aβ plaques in both the hippocampus and cortex. Significantly (p < 0.05) higher CD11b microglial staining was found in the hippocampus, temporal cortex, and frontal cortex of CH-fed versus control rabbits. In the temporal cortex and parietal cortex, active CD-11b- and ferritin-positive microglia were found in close proximity to Aβ plaques. Classification and quantification of activated microglia in the temporal cortex showed that 68±12.9%, 25±7.3%, and 7±2.7% of all microglia had a primed, reactive, and amoeboid phenotype, respectively. Activated microglia also expressed myeloperoxidase which was co-localized to amyloid deposits. Our findings in this dietary-based model lend further support of a role of activated microglia and oxidative stress during the development of AD and strengthens the links between hypercholesterolemia, inflammatory status, and 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".