Lung Transplant Immunomodulation with Genetically Engineered Mesenchymal Stromal Cells—Therapeutic Window for Interleukin-10
Bibliographic record
Abstract
Lung transplantation results are compromised by ischemia–reperfusion injury and alloimmune responses. Ex vivo lung perfusion (EVLP) is used to assess marginal donor lungs before transplantation but is also an excellent platform to apply novel therapeutics. We investigated donor lung immunomodulation using genetically engineered mesenchymal stromal cells with augmented production of human anti-inflammatory hIL-10 (MSCsIL-10). Pig lungs were placed on EVLP for 6 h and randomized to control (n = 7), intravascular delivery of 20 × 106 (n = 5, low dose) or 40 × 106 human MSCs IL-10 (n = 6, high dose). Subsequently, single-lung transplantation was performed, and recipient pigs were monitored for 3 days. hIL-10 secretion was measured during EVLP and after transplantation, and immunological effects were assessed by cytokine profile, T and myeloid cell characterization and mixed lymphocyte reaction. MSCIL-10 therapy rapidly increased hIL-10 during EVLP and resulted in transient hIL-10 elevation after lung transplantation. MSCIL-10 delivery did not affect lung function but was associated with dose-related immunomodulatory effects, with the low dose resulting in a beneficial decrease in apoptosis and lower macrophage activation, but the high MSCIL-10 dose resulting in inflammation and cytotoxic CD8+ T cell activation. MSCIL-10 therapy during EVLP results in a rapid and transient perioperative hIL-10 increase and has a therapeutic window for its immunomodulatory effects.
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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) | 0.000 | 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 | 0.001 |
| 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".