Tissue factor and thrombomodulin expression on endothelial cell‐seeded collagen modules for tissue engineering
Bibliographic record
Abstract
The creation of functional tissue engineering constructs to repair or replace diseased tissues requires a well-formed vasculature network within the construct and the endothelial cells lining that vascular bed must display a nonthrombogenic phenotype. A new approach to tissue engineering involves the assembly of smaller components (modules fabricated at the hundred micron scale) into larger constructs. The modules, collagen gel containing the particular tissue cell of interest, are covered with endothelial cells prior to assembly so that the interconnected channels that are formed are lined with endothelial cells, creating a mimic of a vascular network. Here, we confirmed (using confocal microscopy primarily) that the human umbilical vein endothelial cells, seeded on collagen gel modules without a second embedded cell and without flow, bore the molecular markers of low thrombogenicity. Two days, after seeding on the modules, endothelial cells displayed the typical cobblestone morphology, formed tight cell-cell junctions and covered the whole module surface. Immunofluorescence staining showed that at both 2 days and 7 days after seeding, only a few cells expressed tissue factor while this number was dramatically increased after TNFalpha stimulation. On the other hand, thrombomodulin was expressed by the majority of seeded cells and expression was reduced after TNFalpha stimulation.
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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) | 0.000 | 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".