Increase of blood-brain barrier leakage is related to cognitive decline in vascular mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Blood-brain barrier (BBB) breakdown, as an early biomarker for vascular mild cognitive impairment (vMCI), has only been validated by a few studies. The aim of this study was to investigate whether compromised BBB integrity is involved in vMCI patients, and detect the relationship between BBB breakdown and cognitive function. BBB leakage in vMCI was explored, and the relationship between BBB leakage and cognitive function was discussed in this study. METHODS: This is a cross-sectional study involving 26 vMCI patients and 21 sex- and age-matched healthy controls. Dynamic contrast-enhanced-magnetic resonance imaging was performed for all participants, to determine BBB leakage. Leakage volume, leakage rate, and fractional blood plasma volume (Vp) in the grey and white matter were evaluated. Neuropsychological tests were used to determine cognitive function. Leakage rate, leakage volume, and Vp in different brain locations, including deep grey matter, cortical grey matter, white matter hyperintensity, and normal-appearing white matter were compared between the two groups. RESULTS: Multivariable linear regression analyses revealed that in all regions of interest, the leakage rate was significantly higher in vMCI patients relative to controls. Leakage volume in normal-appearing white matter and white matter hyperintensity were significantly higher, while Vp in normal-appearing white matter, deep grey matter, and cortical grey matter were significantly lower in vMCI patients. Moreover, Montreal Cognitive Assessment scores decreased with the increase of leakage rate in white matter hyperintensity. CONCLUSION: Increased BBB permeability was detected in vMCI patients and was related to cognitive decline, which suggested that BBB breakdown might be involved in cognitive dysfunction pathogenesis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".