Mild Microglial Responses in the Cortex and Perivascular Macrophage Infiltration in Subcortical White Matter in Dogs with Age-Related Dementia Modelling Prodromal Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Microglia contribute to Alzheimer's disease (AD) pathogenesis by clearing amyloid-β (Aβ) and driving neuroinflammation. Domestic dogs with age-related dementia (canine cognitive dysfunction (CCD)) develop cerebral amyloidosis like humans developing AD, and studying such dogs can provide novel information about microglial response in prodromal AD. OBJECTIVE: The aim was to investigate the microglial response in the cortical grey and the subcortical white matter in dogs with CCD versus age-matched cognitively normal dogs. METHODS: Brains from aged dogs with CCD and age-matched controls without dementia were studied. Cases were defined by dementia rating score. Brain sections were stained for Aβ, thioflavin S, hyperphosphorylated tau, and the microglial-macrophage ionized calcium binding adaptor molecule 1 (Iba1). Results were correlated to dementia rating score and tissue levels of Aβ. RESULTS: Microglial numbers were higher in the Aβ plaque-loaded deep cortical layers in CCD versus control dogs, while the coverage by microglial processes were comparable. Aβ plaques were of the diffuse type and without microglial aggregation. However, a correlation was found between the %Iba1 area and insoluble Aβ 42 and N-terminal pyroglutamate modified Aβ(N3pE)-42. The %Iba1 area was higher in white matter, showing phosphorylation of S396 tau, versus grey matter. Perivascular macrophage infiltrates were abundant in the white matter particularly in CDD dogs. CONCLUSION: The results from this study of the microglial-macrophage response in dogs with CCD are suggestive of relatively mild microglial responses in the Aβ plaque-loaded deep cortical layers and perivascular macrophage infiltrates in the subcortical white matter, in prodromal AD.
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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 | 0.000 |
| 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) | 0.000 | 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".