Impact of Cytokine Expression in the Pre-Implanted Donor Lung on the Development of Chronic Lung Allograft Dysfunction Subtypes
Bibliographic record
Abstract
The long-term success of lung transplantation continues to be challenged by the development of chronic lung allograft dysfunction (CLAD). The purpose of this study was to investigate the relationship between cytokine expression levels in pre-implanted donor lungs and the posttransplant development of CLAD and its subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS). Of 109 patients who underwent bilateral lung or heart-lung transplantation and survived for more than 3 months, 50 BOS, 21 RAS and 38 patients with No CLAD were identified by pulmonary function test results. Using donor lung tissue biopsies sampled from each patient, expression levels of IL-6, IL-1β, IL-8, IL-10, interferon-γ and tumor necrosis factor-α mRNA were measured. IL-6 expression levels were significantly higher in pre-implanted lungs of patients that ultimately developed BOS compared to RAS and No CLAD (p = 0.025 and 0.011, respectively). Cox regression analysis demonstrated an association between high IL-6 expression levels and BOS development (hazard ratio = 4.98; 95% confidence interval = 2.42-10.2, p < 0.001). In conclusion, high IL-6 mRNA expression levels in pre-implanted donor lungs were associated with the development of BOS, not RAS. This association further supports the contention that early graft injury impacts on both late graft function and early graft function.
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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) | 0.000 | 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 | 0.000 |
| 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".