Late Breaking Abstract - Secretory phospholipase A2-IIA in lung allografts: association with bile acid aspiration, bacteria and inflammatory mediators.
Bibliographic record
Abstract
Introduction: Secretory phospholipase A2-IIA (sPLA2-IIA) is increased in inflammatory lung diseases. In lung transplant recipients (LTRs), bile acid (BA) aspiration predicts poor outcomes. We sought to study sPLA2-IIA in LTR airways and its relationship with BA, bacteria and inflammatory mediators. Methods: Bronchoalveolar lavage (BAL) and large airway bronchial wash (LABW) were prospectively collected from 138 LTRs at the 3-month post-transplant surveillance bronchoscopy and assayed for sPLA2-IIA by ELISA. LABW BA were tested by tandem mass spectrometry and inflammatory mediators by 48-multiplex. BAL microbiology was monitored. Non-parametric statistical analysis was performed. Results: sPLA2-IIA was quantifiable in 85% of samples, BAL median 376.6 pg/ml (IQR: 37.69-1760), LABW 1797 pg/ml (350-3998). BAL and LABW sPLA2-IIA levels directly correlated (Spearman r=0.66, p<0.0001). sPLA2-IIA correlated with total BA levels (r=0.33 p=0.0002). Samples with BA in upper tertile showed greater sPLA2-IIA: 3160 pg/ml (1319-5051) vs 1440 pg/ml (236-3573) (Mann-Whitney p<0.005). BAL samples positive for bacteria had higher sPLA2-IIA (642.6 pg/ml; 130-3227) compared to negative samples (223.5 pg/ml; 15.65-1151) (p<0.005). sPLA2-IIA correlated with 85% of inflammatory mediators (p<0.05). Conclusion: sPLA2-IIA levels in LTR airways were comparable to those reported in inflammatory lung disorders. sPLA2-IIA independently correlated with aspirated BA and inflammatory mediators and associated with presence of bacteria. These original findings suggest that sPLA2-IIA in LTR airways may serve as a marker of aspiration and infection, and support further investigation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".