Spotlight on adipose tissue as a remarkable stem cell source for regenerative medicine and tissue engineering applications
Bibliographic record
Abstract
Great scientific strides have been achieved upon discovering the great plasticity of adult mesenchymal stem cells. Such stem cells can be harvested from various sources, including subcutaneous adipose tissue which represents an almost ideal cellular reservoir. Cell therapies using adipose‐derived stem/stromal cells (ASCs) are being conducted in an increasing number of clinical trials such as osteoarthristis and congestive heart failure. The other field reaping the benefits of using ASCs is tissue reconstruction. The multipotency of these cells combined with different engineering strategies result in the in vitro production of a wide variety of human tissues. My research team is using ASCs as building blocks for the production of human tissue substitutes, including adipose tissue itself and skin. They feature a rich extracellular matrix produced by the mesenchymal cells themselves upon ascorbic acid stimulation, recreating a very physiological 3D environment. The reconstructed adipose tissues are functional and secrete important cytokines and growth factors such as leptin, Ang‐1, HGF, and VEGF. This secretome is modulated in a dose‐dependent manner following exposure to the inflammatory cytokine TNFα. Also, production of reconstructed skin using ASCs allowed us to investigate the fate of epithelial stem cells in these engineered tissues, before and after grafting. Supported by CIHR and NSERC.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".