Mesenchymal stromal cells in hematopoietic cell transplantation
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are widely recognized to possess potent immunomodulatory activity, as well as to stimulate repair and regeneration of diseased or damaged tissue. These fundamental properties suggest important applications in hematopoietic cell transplantation. Although the mechanisms of therapeutic activity in vivo are yet to be fully elucidated, MSCs seem to suppress lymphocytes by paracrine mechanisms, including secreted mediators and metabolic modulators. Most recently, host macrophage engulfment of apoptotic MSCs has emerged as an important contributor to the immune suppressive microenvironment. Although bone marrow-derived MSCs are the most commonly studied, the tissue source of MSCs may be a critical determinant of immunomodulatory function. The key application of MSC therapy in hematopoietic cell transplantation is to prevent or treat graft-versus-host disease (GVHD). The pathogenesis of GVHD reveals multiple potential targets. Moreover, the recently proposed concept of tissue tolerance suggests a new possible mechanism of MSC therapy for GVHD. Beyond GVHD, MSCs may facilitate hematopoietic stem cell engraftment, which could gain greater importance with increasing use of haploidentical transplantation. Despite many challenges and much doubt, commercial MSC products for pediatric steroid-refractory GVHD have been licensed in Japan, conditionally licensed in Canada and New Zealand, and have been recommended for approval by an FDA Advisory Committee in the United States. Here, we review key historical data in the context of the most salient recent findings to present the current state of MSCs as adjunct cell therapy in hematopoietic cell transplantation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".