Review: The Self-Assembly Approach for Organ Reconstruction by Tissue Engineering
Bibliographic record
Abstract
One must not forget that tissue engineering was first introduced as a life saving procedure for burn patients (1). The successful engraftment of autologous epidermal sheets was the first proof of concept of the powerful technology that we know today (2-4). However this very interesting initial approach fell into some disrepute because of perceived drawbacks and limitations (5, 6). The subsequent efforts in the field followed essentially three "schools" of thought. The first approach consists in the seeding of cells into various gels, which are then reorganized, by the incorporated cells (7-14). Alternatively, a second approach is to seed cells into a scaffold where they will thrive and secrete extracellular matrix (15-17). The scaffold materials are bioresorbable over a wide range of time periods depending on their chemical structures (18-25). A third approach is different since it uses the principle of a tissue template that allows, after implantation, the ingress of cells into the appropriately organized scaffold. Thus, these grafts are acellular and must stimulate the regenerative potential of the tissue wherever they are implanted (26-31). Our group has developed a different and original method for the reconstruction of soft tissues. It takes full advantage of the various intrinsic properties of cells when appropriately cultured. This entails particular media composition and appropriate mechanical straining of these threedimensional structures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.020 |
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".