A novel automated cell-seeding device for tissue engineering of tubular scaffolds: design and functional validation
Why this work is in the frame
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Bibliographic record
Abstract
Obtaining an efficient, uniform and reproducible cell seeding of porous tubular scaffolds constitutes a major challenge for the successful development of tissue-engineered vascular grafts. In this study, a novel automated cell-seeding device utilizing direct cell deposition, patterning techniques and scaffold rotation was designed to improve the cell viability, uniformity and seeding efficiency of tubular constructs. Quantification methods and imaging techniques were used to evaluate these parameters on the luminal and abluminal sides of fibrous polymer scaffolds. With the automated seeding method, a high cell-seeding efficiency (~89%), viability (~85%) and uniformity (~85-92%) were achieved for both aortic smooth muscle cells (AoSMCs) and aortic endothelial cells (AoECs). The duration of the seeding process was < 8 min. Initial cell density, cell suspension in matrix-containing media, duration of seeding process and scaffold rotation were found to affect the seeding efficiency. After few days of culture, a uniform longitudinal and circumferential cell distribution was achieved without affecting cell viability. Both cell types were viable and spread along the fibres after 28 h and 6 days of static incubation. This new automated cell-seeding method for tubular scaffolds is efficient, reliable and meets all the requirements for clinical applicability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it