A novel automated cell-seeding device for tissue engineering of tubular scaffolds: design and functional validation
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".