<i>In vitro</i>elastogenesis: instructing human vascular smooth muscle cells to generate an elastic fiber-containing extracellular matrix scaffold
Bibliographic record
Abstract
Elastic fibers are essential for the proper function of organs including cardiovascular tissues such as heart valves and blood vessels. Although (tropo)elastin production in a tissue-engineered construct has previously been described, the assembly to functional elastic fibers in vitro using human cells has been highly challenging. In the present study, we seeded primary isolated human vascular smooth muscle cells (VSMCs) onto 3D electrospun scaffolds and exposed them to defined laminar shear stress using a customized bioreactor system. Increased elastin expression followed by elastin deposition onto the electrospun scaffolds, as well as on newly formed fibers, was observed after six days. Most interestingly, we identified the successful deposition of elastogenesis-associated proteins, including fibrillin-1 and -2, fibulin-4 and -5, fibronectin, elastin microfibril interface located protein 1 (EMILIN-1) and lysyl oxidase (LOX) within our engineered constructs. Ultrastructural analyses revealed a developing extracellular matrix (ECM) similar to native human fetal tissue, which is composed of collagens, microfibrils and elastin. To conclude, the combination of a novel dynamic flow bioreactor and an electrospun hybrid polymer scaffold allowed the production and assembly of an elastic fiber-containing ECM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".