Mesenchymal Stromal Cells for Graft-Versus-Host Disease
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) have been shown to mediate immune responses in vitro and in vivo. These observations have led to clinical trials of MSC administration to ameliorate acute graft-versus-host disease (GVHD), the most serious complication arising after allogeneic hematopoietic cell transplantation. Clinical data suggest a benefit in approximately two-thirds of patients with steroid-resistant acute GVHD. Preliminary studies have been reported on the use of MSCs to treat de novo acute GVHD, for prophylaxis of the condition, and more recently, in the management of chronic GVHD. Although preclinical data inferred a possible role of MSCs in affecting GVHD mechanisms, more robust animal models became available only after numerous clinical trials with these cells had been undertaken. Further clinical trials, the development of more appropriate animal models and an effective means of tracking and imaging the introduced cells in real time in patients, are required to better define their role in this important area of medicine. Clinical graft versus host disease (GVHD) is a severe inflammatory condition and a major complication of allogeneic hematopoietic cell transplantation. Mesenchymal stromal cells (MSCs) have been shown to mediate immune responses in vitro and in vivo and have been successfully used in clinical trials for acute GVHD. Here, Tolar and colleagues review the scientific and clinical aspects of using MSCs for treating GVHD.
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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 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.006 | 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".