The role of eCyPA as an inflammatory biomarker for predicting 28-day mortality in ARDS patients
Bibliographic record
Abstract
BACKGROUND: Acute respiratory distress syndrome (ARDS) is the primary manifestation of systemic inflammatory response syndrome in the lungs. Extracellular cyclophilin A (eCyPA) as a novel inflammatory marker, this study aims to investigate the expression of eCyPA in ARDS and its relationship with patient prognosis. METHODS: This retrospective study collected serum samples from adult patients diagnosed with acute respiratory distress syndrome (ARDS) upon their admission to ICU. Additionally, venous blood and bronchoalveolar lavage fluid were obtained from a lipopolysaccharide (LPS)-induced ARDS mouse model. The expression levels of extracellular cyclophilin A in serum and bronchoalveolar lavage fluid were quantified using enzyme-linked immunosorbent assay (ELISA). The data collection period extended from December 2021 to December 2023. Binary logistic regression analysis, ROC curve, Kaplan-Meier survival analysis were employed to evaluate the correlation with 28-day all-cause mortality in ARDS patients. An incremental prognostic value analysis was conducted to further investigate the enhancement in predictive value when combined with clinically significant indicators (platelet count and oxygenation index). Additionally, we assessed the dynamic trends of eCyPA and cytokines in serum and bronchoalveolar lavage fluid at 0, 1, 3, and 7 days in LPS-induced ARDS mouse models. RESULTS: A total of 50 patients were enrolled. The serum eCyPA expression level was significantly higher among non-survivors compared to survivors (7.2 ng/ml, interquartile range, IQR, 5.9-8.2 vs. 10.1 ng/ml, IQR 7.5-10.6; p = 0.002), and this finding remained consistent across various subgroups. eCyPA is an effective predictor of 28-day mortality in ARDS patients (AUC = 0.754). When combined with platelet count and oxygenation index, the prognostic value assessment improved the AUC to 0.887 (P < 0.001). In ARDS mice, eCyPA increased continuously over 7 days in serum and bronchoalveolar lavage fluid, the trend is similar to inflammatory cytokines in ARDS mice. CONCLUSIONS: The findings suggest that eCyPA may serve as a potential biomarker for predicting 28-day mortality in patients with acute respiratory distress syndrome.
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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 | 0.002 |
| 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 | 0.001 |
| 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".