Susceptibility to capillary plugging can predict brain region specific vessel loss with aging
Bibliographic record
Abstract
Vessel loss in the aging brain is commonly reported, yet important questions remain concerning whether there are regional vulnerabilities and what mechanisms could account for these regional differences, if they exist. Here we imaged and quantified vessel length, tortuosity and width in 15 brain regions in young adult and aged mice. Our data indicate that vessel loss was most pronounced in white matter followed by cortical, then subcortical grey matter regions, while some regions (visual cortex, amygdala, thalamus) showed no decline with aging. Regions supplied by the anterior cerebral artery were more vulnerable to loss than those supplied by middle or posterior cerebral arteries. Vessel width and tortuosity generally increased with age but neither reliably predicted regional vessel loss. Since capillaries are naturally prone to plugging and prolonged obstructions often lead to vessel pruning, we hypothesized that regional susceptibilities to plugging could help predict vessel loss. By mapping the distribution of microsphere-induced capillary obstructions, we discovered that regions with a higher density of persistent obstructions were more likely to show vessel loss with aging and vice versa. These findings indicate that age-related vessel loss is region specific and can be explained, at least partially, by regional susceptibilities to capillary plugging.
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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) | 0.001 | 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".