Cell Sheet Technology for Tissue Engineering: The Self-Assembly Approach Using Adipose-Derived Stromal Cells
Bibliographic record
Abstract
In the past years, adipose tissue has spurred a wide interest, not only as a source of adult multipotent stem cells but also as a highly eligible tissue for reconstructive surgery procedures. Tissue engineering is one field of regenerative medicine progressing at great strides in part due to its important use of adipose-derived stem/stromal cells (ASCs). The development of diversified technologies combining ASCs with various biomaterials has lead to the reconstruction of numerous types of tissue-engineered substitutes such as bone, cartilage, and adipose tissues from rodent, porcine, or human ASCs. We have recently achieved the reconstruction of connective and adipose tissues composed entirely of cultured human ASCs and their secreted endogenous extracellular matrix components by a methodology known as the self-assembly approach of tissue engineering. The latter is based on the stimulation of ASCs to secrete and assemble matrix components in culture, leading to the production of cell sheets that can be manipulated and further assembled into thicker multilayer tissues. In this chapter, protocols to generate both reconstructed connective and adipocyte-containing tissues using the self-assembly approach are described in detail. The methods include amplification and cell banking of human ASCs, as well as culture protocols for the production of individual stromal and adipose sheets, which are the building blocks for the reconstruction of multilayered human connective and adipose tissues, respectively.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".