351 Phenotypic Modulation of Adipose-Derived Stem Cells Within 3D Scaffolds; Applications in Skin Bioengineering
Bibliographic record
Abstract
Large burn injuries and wounds contribute to increased patient morbidity and mortality and impose a significant financial burden on healthcare systems. The main goal of wound treatment is to achieve a rapid closure of the lesion and promote healing with minimal scarring. Extracellular matrix- based biomaterials such as acellular dermal matrices (ADM) or in situ-forming scaffolds are advantageous for treatment of large wounds due to their mechanical strength and retained biological activity when compared with synthetic polymer materials. Further, recellularization of ADM with adipose-derived stem cells (ASCs) significantly increases the healing capacity. The aim of this study was to develop an ASC-populated ADM and assess its characteristics in vitro with the ultimate goal of engineering an optimized wound healing composite. To this end, we developed a novel method for de-cellularization of skin and used the acquired ADM as a 3D scaffold to seed human ASCs. We compared this 3D model with traditional 2D ASC cultures at different time points post-culture. A panel of cell surface markers was used for phenotypic characterization of ASCs, post-culture. Combinations of positive (CD146, CD44, CD90, and CD73) and negative (CD31, CD34, CD45) cell surface proteins were used as stem cell markers. Morphology and myofibroblast differentiation capacity of ASCs were also evaluated. The results showed a significant reduction in expression of CD73 and CD44 markers in ASCs when embedded within a 3D ADM, compared to the cells that were cultured in a 2D culture condition. We found that ASCs cultured under regular 2D conditions mainly differentiated towards a myofibroblastic phenotype with increased myofibroblast marker α-smooth muscle actin (α-SMA) and type I pro-collagen. In contrast, ASCs cultured on ADM showed a more balanced differentiation pattern with maintenance of important stem cell markers such as CD146. Taken together, these findings suggest that ASC differentiation can be regulated by a 3D scaffold. Embedding ASCs within 3D scaffolds prevents universal differentiation to stromal cells and maintain stemness features associated with ASCs. The ASC-ADM combination shows a promising potential as a novel therapeutic approach for treatment of large burn injuries and wounds. This bioengineered composite can reduce risk of fibrosis while maintaining the regenerative capacity of stem cells.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".