The role of extracellular vesicles in chronic lung allograft dysfunction and response to extracorporeal photopheresis
Bibliographic record
Abstract
Current research highlights the growing role of extracellular vesicles (EV) in mechanisms of lung allograft dysfunction. In particular, EVs are involved in antigen presentation, where they are released from lung allografts and express tissue associated antigens which are recognized by recipient immune cells, thereby triggering an immune response against the transplanted lung. In the context of chronic rejection, patients with chronic lung allograft dysfunction (CLAD) demonstrate elevated levels of EVs, which contain diverse molecular cargo that can influence the alloimmune response. This highlights the potential of EVs as translatable biomarkers for the early detection, prediction, or diagnosis of lung allograft dysfunction. The mechanisms by which EVs contribute to this process may include immune cell activation, epithelial-to-mesenchymal transition, and disruption of angiogenesis. Furthermore, their immunomodulatory potential is evident by their emerging involvement in regulating the immune response during extracorporeal photopheresis (ECP) therapy following lung transplantation, where they contribute to the balance of immunoregulatory and autoimmune responses within a highly interwoven network. While ECP shows promise for broader or earlier use in solid organ transplantation, its application is limited by a lack of mechanistic understanding. This review summarizes the role of EVs in development of lung allograft dysfunction, their involvement in immunomodulation, and the current literature exploring their potential role in the mechanisms of ECP therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".