Mesenchymal stromal cells in hematopoietic cell transplantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it