A planar model of the vessel wall from cellularized-collagen scaffolds: focus on cell–matrix interactions in mono-, bi- and tri-culture models
Bibliographic record
Abstract
The acquisition of new thorough knowledge on the interactions existing between vascular cells would represent a step forward in the engineering of vascular tissues. In this light, herein we designed a physiological-like tri-culture in vitro vascular wall model using a planar cellularized collagen gel as the scaffold. The model can be obtained in 24 h and features multi-layered hierarchical organization composed of a fibroblast-containing adventitia-like layer, a media-like layer populated by smooth muscle cells and an intima-like endothelial cell monolayer. After 7 days of static culture, the compaction of the collagen matrix by the vascular cells was achieved, and the deposition of the vascular extracellular matrix components fibronectin, fibrillin-1 and tropoelastin was observed. The blood-compatible functionality of the endothelial cell monolayer was demonstrated by a blood clotting assay: after 7 days of maturation, clotting was prevented on the endothelialized constructs (more than 80% free hemoglobin maintained after 60 min of blood contact) but not at all on non-endothelialized ones (less than 20% free hemoglobin). In addition, western blotting results suggested that in the tri-culture model the loss of smooth muscle cell phenotype was delayed compared to what was observed in the mono-culture model, finally resulting in a behaviour more similar to the in vivo conditions. Overall, our findings indicate that this in vitro model has the potential to be used as an advanced system to examine vascular cell behavioural interactions, as well as for drug testing and the investigation of physiological and pathological processes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".