Reconstruction of Vascular and Urologic Tubular Grafts by Tissue Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 it