Differential perivascular microglial activation in the deep white matter in vascular dementia developed post‐stroke
Bibliographic record
Abstract
Abstract With the hypothesis that perivascular microglia are involved as neuroinflammatory components of the gliovascular unit contributing to white matter hyperintensities on MRI and pathophysiology, we assessed their status in stroke survivors who develop dementia. Immunohistochemical and immunofluorescent methods were used to assess the distribution and quantification of total and perivascular microglial cell densities in 68 brains focusing on the frontal lobe WM and overlying neocortex in post‐stroke dementia (PSD), post‐stroke non‐dementia (PSND) and similar age control subjects. We primarily used CD68 as a marker of phagocytic microglia, as well as other markers of microglia including Iba‐1 and TMEM119, and the myeloid cell marker TREM2 to assess dementia‐specific changes. We first noted greater total densities of CD68+ and TREM2+ cells per mm2 in the frontal WM compared to the overlying cortex across the stroke cases and controls (p = 0.001). PSD subjects showed increased percentage of activated perivascular CD68+ cells distinct from ramified or primed microglia in the WM (p < 0.05). However, there was no apparent change in perivascular TREM2+ cells. Total densities of TREM2+ cells were only ~10% of CD68+ cells but there was high degree of overlap (>70%) between them in both the WM and the cortex. CD68 and Iba‐1 or CD68 and TMEM119 markers were colocalised by ~55%. Within the deep WM, ~30% of CD68+ cells were co‐localised with fragments of degraded myelin basic protein. Among fragmented CD68+ cells in adjacent WM of PSD subjects, >80% of the cells expressed cleaved caspase‐3. Our observations suggest although the overall repertoire of perivascular microglial cells is not changed in the parenchyma, PSD subjects accrue more perivascular‐activated CD68+ microglia rather than TREM2+ cells. This implies there is a subset of CD68+ cells, which are responsible for the differential response in perivascular inflammation within the gliovascular unit of the deep WM.
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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".