Clinical grade allogeneic human mesenchymal stem cells restore alveolar fluid clearance in human lungs rejected for transplantation
Bibliographic record
Abstract
The lack of suitable donors for all solid-organ transplant programs is exacerbated in lung transplantation by the low utilization of potential donor lungs, due primarily to donor lung injury and dysfunction, including pulmonary edema. The current studies were designed to determine if intravenous clinical-grade human mesenchymal stem (stromal) cells (hMSCs) would be effective in restoring alveolar fluid clearance (AFC) in the human ex vivo lung perfusion model, using lungs that had been deemed unsuitable for transplantation and had been subjected to prolonged ischemic time. The human lungs were perfused with 5% albumin in a balanced electrolyte solution and oxygenated with continuous positive airway pressure. Baseline AFC was measured in the control lobe and if AFC was impaired (defined as <10%/h), the lungs received either hMSC (5 × 10(6) cells) added to the perfusate or perfusion only as a control. AFC was measured in a different lung lobe at 4 h. Intravenous hMSC restored AFC in the injured lungs to a normal level. In contrast, perfusion only did not increase AFC. This positive effect on AFC was reduced by intrabronchial administration of a neutralizing antibody to keratinocyte growth factor (KGF). Thus, intravenous allogeneic hMSCs are effective in restoring the capacity of the alveolar epithelium to remove alveolar fluid at a normal rate, suggesting that this therapy may be effective in enhancing the resolution of pulmonary edema in human lungs deemed clinically unsuitable for 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".