Plasma CXCL9 and CXCL10 at allograft injury predict chronic lung allograft dysfunction
Bibliographic record
Abstract
Histopathologic lung allograft injuries are putative harbingers for chronic lung allograft dysfunction (CLAD). However, the mechanisms responsible are not well understood. CXCL9 and CXCL10 are potent chemoattractants of mononuclear cells and potential propagators of allograft injury. We hypothesized that these chemokines would be quantifiable in plasma, and would associate with subsequent CLAD development. In this prospective multicenter study, we evaluated 721 plasma samples for CXCL9/CXCL10 levels from 184 participants at the time of transbronchial biopsies during their first-year post-transplantation. We determined the association between plasma chemokines, histopathologic injury, and CLAD risk using Cox proportional hazards models. We also evaluated CXCL9/CXCL10 levels in bronchoalveolar lavage (BAL) fluid and compared plasma to BAL with respect to CLAD risk. Plasma CXCL9/CXCL10 levels were elevated during the injury patterns associated with CLAD, acute rejection, and acute lung injury, with a dose-response relationship between chemokine levels and CLAD risk. Importantly, there were strong interactions between injury and plasma CXCL9/CXCL10, where histopathologic injury associated with CLAD only in the presence of elevated plasma chemokines. We observed similar associations and interactions with BAL CXCL9/CXCL10 levels. Elevated plasma CXCL9/CXCL10 during allograft injury may contribute to CLAD pathogenesis and has potential as a minimally invasive immune monitoring biomarker.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".