<i>Ex vivo</i> delivery of regulatory T-cells for control of alloimmune priming in the donor lung
Bibliographic record
Abstract
Background Survival after lung transplantation (LTx) is hampered by uncontrolled inflammation and alloimmunity. Regulatory T-cells (Tregs) are being studied as a cellular therapy in solid organ transplantation. Whether these systemically administered Tregs can function at the appropriate location and time is an important concern. We hypothesised that in vitro -expanded recipient-derived Tregs can be delivered to donor lungs prior to LTx via ex vivo lung perfusion (EVLP), maintaining their immunomodulatory ability. Methods In a rat model, Wistar Kyoto (WKy) CD4 + CD25 high Tregs were expanded in vitro prior to EVLP. Expanded Tregs were administered to Fisher 344 (F344) donor lungs during EVLP; left lungs were transplanted into WKy recipients. Treg localisation and function post-transplant were assessed. In a proof-of-concept experiment, cryopreserved expanded human CD4 + CD25 + CD127 low Tregs were thawed and injected into discarded human lungs during EVLP. Results Rat Tregs entered the lung parenchyma and retained suppressive function. Expanded Tregs had no adverse effect on donor lung physiology during EVLP; lung water as measured by wet-to-dry weight ratio was reduced by Treg therapy. The administered cells remained in the graft at 3 days post-transplant where they reduced activation of intra-graft effector CD4 + T-cells; these effects were diminished by day 7. Human Tregs entered the lung parenchyma during EVLP where they expressed key immunoregulatory molecules (CTLA4 + , 4-1BB + , CD39 + and CD15s + ). Conclusions Pre-transplant Treg administration can inhibit alloimmunity within the lung allograft at early time points post-transplant. Our organ-directed approach has potential for clinical translation.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".