The Impact of Cell-Expansion and Inflammation on The Immune-Biology of Human Adipose Tissue-Derived Mesenchymal Stromal Cells
Bibliographic record
Abstract
Background: As a cell-based therapeutic, AT-MSCs need to create an immuno-reparative environment appropriate for tissue repair. In the presence of injury, MSCs may have to proliferate and face inflammation. Clinical application requires repeated administrations of a high number of cells with a well-established immune profile. Methods: We have established an immuno-comparative screening by determining the expression of 28 molecules implicated in immune regulation. This screening was performed during cell-expansion and inflammatory priming of AT-MSCs. Results: Our study confirms that AT-MSCs are highly expandable and sensitive to inflammation. Both conditions have substantially modulated the expression of a panel of immunological marker. Specifically, CD34 expression was substantially decreased upon cell-passaging. HLA-ABC, CD40 CD54, CD106, CD274 and CD112 were significantly increased by inflammation. In vitro cell-expansion also significantly altered the expression profile of HLA-DR, CD40, CD62L, CD106, CD166, HLA-G, CD200, HO-1, CD155 and ULBP-3. Conclusion: This study points out the response and characteristics of MSCs following expansion and inflammatory priming. It will strength our knowledge about the molecular mechanisms that may improve or hamper the therapeutic potential of MSCs. These immunological changes need to be further characterized to guarantee a safe cellular product with consistent quality and high therapeutic efficacy.
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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 | 0.000 | 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) | 0.001 | 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 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".