Detection of Cerebrovascular Loss in the Normal Aging C57BL/6 Mouse Brain Using in vivo Contrast-Enhanced Magnetic Resonance Angiography
Bibliographic record
Abstract
Microvascular rarefaction, or the decrease in vascular density, has been described in the cerebrovasculature of aging humans, rats and, more recently, mice in the presence and absence of age-dependent diseases. Given the wide use of mice in modeling age-dependent human diseases of the cerebrovasculature, visualization and quantification of the global murine cerebrovasculature is necessary in establishing the baseline changes that occur with aging. In order to provide in vivo whole brain imaging of the cerebrovasculature in aging C57BL/6 mice longitudinally, contrast-enhanced magnetic resonance angiography (CE-MRA) was employed using a house-made gadolinium-bearing micellar blood pool agent. Enhancement in the vascular space permitted quantification of the detectable, or apparent, cerebral blood volume (aCBV), which was analyzed over 2 years of aging and compared to histological analysis of the cerebrovascular density. A significant loss in the aCBV was detected by CE-MRA over the aging period. Histological analysis via vessel-probing immunohistochemistry confirmed a significant loss in the cerebrovascular density over the same 2-year aging period, validating the CE-MRA findings. While these techniques use widely different methods of assessment and spatial resolutions, their similarity in detected vascular loss corroborates the growing body of literature describing vascular rarefaction in aging and suggests that such age-dependent changes can contribute to cerebrovascular and neurodegenerative diseases, which are modeled using wild-type and transgenic laboratory rodents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".