Blood Vessel Maturation in Health and Disease and its Implications for Vascularization of Engineered Tissues
Bibliographic record
Abstract
Engineered blood vessels have often been found to be immature and unstable. Similarly, numerous pathologies such as diabetic retinopathy and cancer are characterized by highly abnormal, defective, hypervascular networks, consisting of immature, leaky, and irregular vessels with a marked loss of perivascular cell coverage. An emerging therapeutic concept in treatment of such vascular diseases and their management is the potential to normalize blood vessels by strengthening the cellular components that form the vascular network. Vessel normalization is characterized by the reduction in the number and size of immature vessels, a decrease in interstitial fluid pressure, and increase in perivascular cell coverage. Understanding the molecular and cellular defects associated with abnormal blood vessels will allow us to find appropriate treatment options that can promote normal blood vessel development. These, in turn, can be applied to improve vessel maturation in engineered tissues. In this review, we describe the major perivascular abnormalities associated with various human diseases and engineered vasculatures and the major advances in obtaining mature vasculatures for translational applications.
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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 | 0.000 |
| 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 | 0.001 |
| 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 | 0.001 |
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".