Concise Review: Hitting the Right Spot with Mesenchymal Stromal Cells
Bibliographic record
Abstract
Mesenchymal stromal cells or mesenchymal stem cells (MSCs) have captured considerable scientific and public interest because of their potential to limit physical and immune injury, to produce bioactive molecules and to regenerate tissues. MSCs are phenotypically heterogeneous and distinct subpopulations within MSC cultures are presumed to contribute to tissue repair and the modulation of allogeneic immune responses. As the first example of efficacy, clinical trials for prevention and treatment of graft-versus-host disease after hematopoietic cell transplantation show that MSCs can effectively treat human disease. The view of the mechanisms whereby MSCs function as immunomodulatory and reparative cells has evolved simultaneously. Initially, donor MSCs were thought to replace damaged cells in injured tissues of the recipient. More recently, however, it has become increasingly clear that even transient MSC engraftment may exert favorable effects through the secretion of cytokines and other paracrine factors, which engage and recruit recipient cells in productive tissue repair. Thus, an important reason to investigate MSCs in mechanistic preclinical models and in clinical trials with well-defined end points and controls is to better understand the therapeutic potential of these multifunctional cells. Here, we review the controversies and recent insights into MSC biology, the regulation of alloresponses by MSCs in preclinical models, as well as clinical experience with MSC infusions (Table 1) and the challenges of manufacturing a ready supply of highly defined transplantable MSCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".