Independent human mesenchymal stromal cell-derived extracellular vesicle preparations differentially affect symptoms in an advanced murine Graft-versus-Host-Disease model
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) harvested from cell culture supernatants of human mesenchymal stromal cells (MSCs) suppress acute inflammation in preclinical models of various diseases. Furthermore, they promote regeneration of damaged tissues. Following successful clinical treatment of a steroid-refractory Graft-versus-Host-Disease (GvHD) patient with EVs prepared from conditioned media of human bone marrow (BM)-derived MSCs, we aim to improve MSC-EV production and quality control towards clinical application. Observing functional differences of independent MSC-EV preparations in vitro , we established an optimized murine GvHD model for the analysis of independent MSC-EV preparations in vivo . To this end, T cell depleted allogeneic BM cells co-transplanted with naïve allogeneic spleen-derived T cells induced GvHD symptoms with reproducible strengths in mice being preconditioned by ionizing irradiation. Administration of MSC-EV preparations with confirmed in vitro immune modulatory properties at three consecutive days significantly suppressed GvHD symptoms. In contrast, application of MSC-EV preparations lacking these in vitro immune modulating capabilities failed to suppress GvHD symptoms. Thus, our results reveal therapeutic differences among independent MSC-EV preparations that had been produced in a standardized manner. Thus, given this functional heterogeneity, any individual MSC-EV preparation considered for the clinical application should be evaluated for its potency prior to administration to patients.
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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) | 0.001 | 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".