Intragraft Hyaluronan Increases in Association With Acute Lung Transplant Rejection
Bibliographic record
Abstract
Background. Acute perivascular rejection (AR) is common in lung recipients and increases the risk for chronic lung allograft dysfunction (CLAD). Hyaluronan (HA), an extracellular matrix constituent, accumulates in experimental AR and can act as an innate immune agonist, breaking tolerance and potentiating alloimmunity. We previously demonstrated HA accumulates in CLAD after human-lung transplantation. We sought to determine if HA accumulates in the bronchoalveolar lavage fluid (BALF) concurrent with AR in lung recipients. Methods. The cohort consisted of 126 first adult lung recipients at 5 transplant centers with a total of 373 BALF samples collected within the first posttransplant year. All samples were paired with a lung biopsy from the same bronchoscopy. BALF HA (ng/mL) was quantified by ELISA and log-transformed for analysis. Linear-mixed effect models, adjusted for potential confounders, were used to estimate the association between BALF HA concentration and the presence of AR on biopsy. The association between early posttransplant BALF HA levels and the development of CLAD was explored utilizing tertiles of maximum BALF HA level observed within the first 6 months of transplant. Results. In analyses adjusted for potential confounders, BALF HA concentration was significantly increased in association with AR (change in means on log-scale 0.31; 95% CI, 0.01-0.60; P = 0.044). When considered on the original scale (ng/mL), BALF HA concentrations were 1.36 times (36%) higher, on average, among samples with, versus without, AR. The cumulative incidence of CLAD was numerically higher in individuals in the highest tertiles of BALF HA level within the first 6 months after transplant, as compared with those in the lowest tertile; however, this difference was not statistically significant (P = 0.32). Conclusions. These results demonstrate accumulation of HA in clinical AR and suggest a mechanism by which innate and adaptive immune activation might interact in the development of AR and CLAD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".