Brain mural cell loss in the parietal cortex in Alzheimer’s disease correlates with cognitive decline and TDP‐43 pathology
Bibliographic record
Abstract
AIMS: Brain mural cells (BMC), smooth muscle cells and pericytes, interact closely with endothelial cells and modulate numerous cerebrovascular functions. A loss of BMC function is suspected to play a role in the pathophysiology of Alzheimer's Disease (AD). METHODS: BMC markers, namely smooth muscle alpha actin (α-SMA) for smooth muscle cells, as well as platelet-derived growth factor receptor β (PDGFRβ) and aminopeptidase N (ANPEP or CD13) for pericytes, were assessed by Western immunoblotting in microvessel extracts from the parietal cortex of 60 participants of the Religious Orders study, with ages at death ranging from 75 to 98 years old. RESULTS: Participants clinically diagnosed with AD had lower vascular levels of α-SMA, PDGFRβ and CD13. These reductions were correlated with lower cognitive scores for global cognition, episodic and semantic memory, perceptual speed and visuospatial ability. In addition, α-SMA, PDGFRβ and CD13 were negatively correlated with vascular Aβ40 concentrations. Vascular levels of BMC markers were also inversely correlated with insoluble cleaved phosphorylated transactive response DNA binding protein 43 (TDP-43) (25 kDa) and positively correlated with soluble cleaved phosphorylated TDP-43 (35 kDa) in cortical homogenates, suggesting strong association between BMC loss and cleaved phosphorylated TDP-43 aggregation. CONCLUSIONS: The results of this study highlight a loss of BMC in AD. The associations between α-SMA, PDGFRβ and CD13 vascular levels with cognitive scores, TDP-43 aggregation and cerebrovascular accumulation of Aβ in the parietal cortex suggest that BMC loss contributes to both AD symptoms and pathology, further strengthening the link between cerebrovascular defects and dementia.
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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 | 0.001 |
| 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) | 0.002 | 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".