Engineering Tissues without the Use of a Synthetic Scaffold: A Twenty-Year History of the Self-Assembly Method
Bibliographic record
Abstract
Twenty years ago, Dr. François A. Auger, the founder of the Laboratory of Experimental Organogenesis (LOEX), introduced the self-assembly technique. This innovative technique relies on the ability of dermal fibroblasts to produce and assemble their own extracellular matrix, differing from all other tissue-engineering techniques that use preformed synthetic scaffolds. Nevertheless, the use of the self-assembly technique was limited for a long time due to its main drawbacks: time and cost. Recent scientific breakthroughs have addressed these limitations. New protocol modifications that aim at increasing the rate of extracellular matrix formation have been proposed to reduce the production costs and laboratory handling time of engineered tissues. Moreover, the introduction of vascularization strategies in vitro permits the formation of capillary-like networks within reconstructed tissues. These optimization strategies enable the large-scale production of inexpensive native-like substitutes using the self-assembly technique. These substitutes can be used to reconstruct three-dimensional models free of exogenous materials for clinical and fundamental applications.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".