Cerebral Microbleeds Are Associated with Widespread Blood-Brain Barrier Leakage
Bibliographic record
Abstract
INTRODUCTION: The pathogenesis of cerebral microbleeds (CMBs) is incompletely understood, but blood-brain barrier (BBB) leakage may play a key role. This study aimed to investigate the relationship between compromised BBB integrity and CMBs as well as cognitive function. METHODS: Ninety-seven participants were enrolled in this cross-sectional study, involving 24 CMB patients. Dynamic contrast-enhanced-magnetic resonance imaging was used to measure BBB permeability, and cognitive function was assessed by Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). RESULTS: Compared with participants without CMBs, CMB patients had higher volume transfer constant (Ktrans, all p < 0.01) and area under the concentration curve (AUC, all p < 0.05) in normal-appearing white matter (NAWM), white matter hyperintensities (WMH), cortical gray matter (CGM), and deep gray matter (DGM). Multivariable linear regression analyses revealed that CMB patients had significantly higher Ktrans in NAWM and AUC in NAWM, WMH, and CGM after adjustment for age, sex, vascular risk factors, and cognitive scores. MMSE and MoCA scores decreased with increasing Ktrans in WMH and DGM as well as AUC in WMH after adjustment for age, sex, CMB group, and education length. CONCLUSION: This study demonstrated that widespread BBB leakage was prevalent in CMB patients, suggesting that compromised BBB integrity may play a key role in the pathogenesis of CMBs and could lead to cognitive impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".