Spherical rotary cell seeding system for production of small-caliber tissue-engineered blood vessels with complex geometry
Bibliographic record
Abstract
Entirely biological human tissue-engineered blood vessels (TEBV) were previously developed for clinical use. Tissue-engineered models have also proven to be valuable tools in disease modelling. Moreover, there is a need for complex geometry TEBV for study of multifactorial vascular pathologies, such as intracranial aneurysms. The main goal of the work reported in this article was to produce an entirely human branched small-caliber TEBV. The use of a novel spherical rotary cell seeding system allows effective and uniform dynamic cell seeding for a viable in vitro tissue-engineered model. In this report, the design and fabrication of an innovative seeding system with random spherical 360° rotation is described. Custom made seeding chambers are placed inside the system and hold Y-shaped polyethylene terephthalate glycol (PETG) scaffolds. The seeding conditions, such as cell concentration, seeding speed and incubation time were optimized via count of cells adhered on the PETG scaffolds. This spheric seeding method was compared to other approaches, such as dynamic and static seeding, and clearly shows uniform cell distribution on PETG scaffolds. With this simple to use spherical system, fully biological branched TEBV constructs were also produced by seeding human fibroblasts directly on custom-made complex geometry PETG mandrels. The production of patient-derived small-caliber TEBVs with complex geometry and optimized cellular distribution all along the vascular reconstructed may be an innovative way to model various vascular diseases such as intracranial aneurysms.
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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) | 0.000 | 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".