Endothelial-to-mesenchymal transition in the central nervous system: A potential therapeutic target to combat age-related vascular fragility
Bibliographic record
Abstract
Age-related dysfunction of the central nervous system, including cognitive impairment and visual disorders, is a major concern for the aging population, affecting health span and quality of life. Age-related vascular dysfunction in the central nervous system includes an increase in blood-brain or blood-retina barrier permeability, an increase in vascular fragility, and impaired neurovascular coupling, contributing to cognitive impairment and vision loss. While these pathologies occur in the brain and eye with age, gaps remain in our understanding of the underlying cellular mechanisms. During the process of endothelial-to-mesenchymal transition (EndMT), endothelial cells lose their characteristic endothelial phenotypes, which are critical for vascular function, such as barrier integrity, and transition to a mesenchymal-like phenotype. EndMT is triggered by many age-related stimuli and is involved in the progression of many age-related diseases (eg, atherosclerosis, cardiovascular disease, etc). Here, we review what is known about the role of EndMT in vascular fragility in the aging brain and eye, explore the mechanistic links between endothelial cell transdifferentiation and age-associated vascular pathologies of the central nervous system, and identify potential therapeutic targets ripe for future exploration with the goal of preserving vascular function with aging by regulating EndMT. SIGNIFICANCE STATEMENT: Endothelial-to-mesenchymal transition is a key form of cellular plasticity that leads to disrupted barrier function and vascular disorders. Here, we evaluate what is known about this process in the brain, highlight potential targetable mechanisms to block it, and identify areas where further research is needed.
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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 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".