Adipose tissue engineering with cells in engineered matrices
Bibliographic record
Abstract
Tissue engineering has shown promise for the development of constructs to facilitate large volume soft tissue augmentation in reconstructive and cosmetic plastic surgery. This article reviews the key progress to date in the field of adipose tissue engineering. In order to effectively design a soft tissue substitute, it is critical to understand the native tissue environment and function. As such, the basic physiology of adipose tissue is described and the process of adipogenesis is discussed. In this article, we have focused on tissue engineering using a cell-seeded scaffold approach, where engineered extracellular matrix substitutes are seeded with exogenous cells that may contribute to the regenerative response. The strengths and limitations of each of the possible cell sources for adipose tissue engineering, including adipose-derived stem cells, are detailed. We briefly highlight some of the results from the major studies to date, involving a range of synthetic and naturally derived scaffolds. While these studies have shown that adipose tissue regeneration is possible, more research is required to develop optimized constructs that will facilitate safe, predictable and long-term augmentation in clinical applications.
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 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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".