The Effect of Dynamical Strain on the Maturation of Collagen-Based Cell-Containing Scaffolds for Vascular Tissue Engineering
Bibliographic record
Abstract
Diseases occurring to blood vessel are preferentially solved by replacing the vessel by an autologous graft. When it is not available, a synthetic graft is used which has low patency rates for small diameter (<6 mm) vessels. Tissue engineering of blood vessel aims to improve the performance of vascular substitutes. Bioreactors are used in vascular tissue engineering to mimic the mechanical and biochemical environment of blood vessel. A 2D bioreactor was custom made in order to impose a dynamical strain to silicone membrane receiving the collagen cell-based construct. Collagen gels with vascular smooth muscle cells cultured inside were subdued to maturation under dynamical uniaxial stretch regimes at 1Hz for 48 hours. The percentage of deformation encountered by the silicone membrane was measured by ImageJ. Collagen fibrils and porcine smooth muscle cells (PSMC) orientations were assessed by scanning electron microscopy (SEM). Results show that the study of mechanical conditioning on cell activity is an important issue for enhancing the alignment of collagen fibrils.
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 | 0.000 | 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".