Converging Perturbed Microvasculature and Microglial Clusters Characterize Alzheimer Disease Brain
Bibliographic record
Abstract
We have investigated physical properties of microvasculature and vessel association with microglial clusters in cortical tissue from Alzheimer disease individuals, classified as severe (ADsev) or mild (ADmild), and nondemented controls (ND). Immunostaining with laminin or von Willerbrand factor demonstrated numbers of microvessels and microvascular density were significantly higher in ADsev cases compared with levels in ADmild or ND cases suggesting proangiogenic activity in ADsev brain. Evidence for extravascular laminin immunoreactivity was found in ADsev tissue and was largely absent in ADmild and ND cases suggesting vascular remodeling in ADsev brain included abnormalities in blood vessels. Microgliosis was progressively increased from ND to ADmild to ADsev with the latter demonstrating areas of clustered microglia (groupings of three or more cells) rarely observed in ADmild or ND cases. Microglial clusters in ADsev brain were in close proximity with extravascular laminin and also plasma protein, fibrinogen, implicating vascular perturbation as a component of inflammatory reactivity. ADsev brain also exhibited elevated levels of the pro-inflammatory/angiogenic factors tumor necrosis factor-α (TNF-α) and vascular endothelial growth factor (VEGF) in association, relative to non-association, with microglial clusters. The presence of extravascular laminin and fibrinogen and the vascular modifying factors, TNF-α and VEGF in localization with clusters of activated microglia, is consistent with microglial-induced vascular remodeling in ADsev brain. Microglial-vascular reciprocal interactions could serve a critical role in the amplification and perpetuation of inflammatory reactivity in AD brain.
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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".