Quantitative Vascular Pathology and Phenotyping Familial and Sporadic Cerebral Small Vessel Diseases
Bibliographic record
Abstract
We quantified vascular changes in the frontal lobe and basal ganglia of four inherited small vessel diseases (SVDs) including cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), pontine autosomal dominant microangiopathy and leukoencephalopathy (PADMAL), hereditary multi-infarct dementia of Swedish type (Swedish hMID), and hereditary endotheliopathy with retinopathy, nephropathy, and stroke (HERNS). Vascular pathology was most severe in CADASIL, and varied with marginally greater severity in the basal ganglia compared to the frontal lobe. The overall sclerotic index values in frontal lobe were in the order CADASIL ≥ HERNS > PADMAL > Swedish hMID > sporadic SVD, and in basal ganglia CADASIL > HERNS > Swedish hMID > PADMAL> sporadic SVD. The subcortical white matter was almost always more affected than any gray matter. We observed glucose transporter-1 (GLUT-1) protein immunoreactivities were most affected in the white matter indicating capillary degeneration whereas collagen IV (COL4) immunostaining was increased in PADMAL cases in all regions and tissue types. Overall, GLUT-1 : COL4 ratios were higher in the basal ganglia indicating modifications in capillary density compared to the frontal lobe. Our study shows that the extent of microvascular degeneration varies in these genetic disorders exhibiting common end-stage pathologies but is the most aggressive in CADASIL.
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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 | 0.002 | 0.001 |
| 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".