Endothelial Cell Senescence Effect on the Blood-Brain Barrier in Stroke and Cognitive Impairment
Bibliographic record
Abstract
Age is an important risk factor of stroke, cognitive decline, and dementia. Senescent endothelial cells (ECs) accumulate with advancing age through exposure to cellular stress, such as that exerted by hypertension and diabetes. These senescent ECs have altered characteristics, such as altered tight junction proteins, use of a more indiscriminate transcellular transport system, increased inflammation, and increased immune cell interactions. ECs are the main component of the blood-brain barrier (BBB), separating the brain from systemic circulation. As senescent ECs accumulate in the BBB, their altered functioning results in the disruption of the barrier. They have inadequate barrier-forming properties, disrupted extracellular matrix, and increased transcytosis, resulting in an overly permeable barrier. This disruption of the BBB can have important effects in stroke and cognitive impairment, as presented in this review. Besides increasing the permeability of the BBB, senescent ECs can also impair angiogenesis and vascular remodeling, which in ischemic stroke may increase risk of hemorrhagic transformation and worsen outcomes. Senescent ECs may also contribute to microvascular dysfunction, with disruption of cerebral perfusion and autoregulation. These may contribute to vascular cognitive impairment along with increased permeability. With an aging population, there is growing interest in targeting senescence. Several ongoing trials have been evaluating whether senolytics can slow aging, improve vascular health, and reduce the risk of stroke and cognitive decline.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".