The structure of the perivascular compartment in the old canine brain: a case study
Bibliographic record
Abstract
Dilatation of periarteriolar spaces in MRI of the ageing human brains occurs in white matter (WM), basal ganglia and midbrain but not in cerebral cortex. Perivenous collagenous occurs in periventricular but not in subcortical WM.Here we test the hypotheses that (a) the capacity for dilatation of periarteriolar spaces correlates with the anatomical distribution of leptomeningeal cells coating intracerebral arteries and (b) the regional development of perivenous collagenous in the WM correlates with the population of intramural cells in the walls of veins.The anatomical distribution of leptomeningeal and intramural cells related to cerebral blood vessels is best documented by electron microscopy, requiring perfusion-fixed tissue not available in human material. We therefore analysed perfusion-fixed brain from a 12-year-old Beagle dog as the canine brain represents the anatomical arrangement in the human brain. Results showed regional variation in the arrangement of leptomeningeal cells around blood vessels. Arterioles are enveloped by one complete layer of leptomeninges often with a second incomplete layer in the WM. Venules showed incomplete layers of leptomeningeal cells. Intramural cell expression was higher in the post-capillary venules of the subcortical WM when compared with periventricular WM, suggesting that periventricular collagenosis around venules may be due to a lower resistance in the venular walls. It appears that the regional variation in the capacity for dilatation of arteriolar perivascular spaces in the white WM may be related to the number of perivascular leptomeningeal cells surrounding vessels in different areas of the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".