TISSUE-ENGINEERED BLOOD VESSELS AND THE FUTURE OF TISSUE SUBSTITUTES The self-assembly as a novel approach to tissue-engineering
Bibliographic record
Abstract
It is seldom acknowledged that the very future of tissue engineering hinges upon the successful reproduction of the cardiovascular system. Since the tissue engineering of the heart is discussed elsewhere in this volume, the focus of the present chapter will be on the vascular system. The previous axiom has been clearly extolled in many physiology textbook, but has unfortunately received only recently all the scrutiny it deserves from tissue engineers. Thus, if the brain is the command centre, the human body must have an extremely efficient import/export system for the nutrition of all its tissues with the accompanying disposal of waste or toxic by-products of the normal metabolism. Our own group (LOEX) has been made keenly aware of the paramount role of vasculature by our clinicians. This has been so because every new project we put in place within our research team involve: biomedical biologists, bioengineers, and physicians specialised in the targeted organ. Thus the importance of reproducing functional vascular substitutes was frequently raised in many projects. These discussions were the impetus for our ongoing interest in tissue engineered blood vessels (TEBV) since 1989.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".