Reconstruction of Vascular and Urologic Tubular Grafts by Tissue Engineering
Bibliographic record
Abstract
Tissue engineering is one of the most promising scientific breakthroughs of the late 20th century. Its objective is to produce in vitro tissues or organs to repair and replace damaged ones using various techniques, biomaterials, and cells. Tissue engineering emerged to substitute the use of native autologous tissues, whose quantities are sometimes insufficient to correct the most severe pathologies. Indeed, the patient’s health status, regulations, or fibrotic scars at the site of the initial biopsy limit their availability, especially to treat recurrence. This new technology relies on the use of biomaterials to create scaffolds on which the patient’s cells can be seeded. This review focuses on the reconstruction, by tissue engineering, of two types of tissue with tubular structures: vascular and urological grafts. The emphasis is on self-assembly methods which allow the production of tissue/organ substitute without the use of exogenous material, with the patient’s cells producing their own scaffold. These continuously improved techniques, which allow rapid graft integration without immune rejection in the treatment of severely burned patients, give hope that similar results will be observed in the vascular and urological fields.
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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".