Soluble platelet-endothelial cell adhesion molecule-1, a biomarker of ventilator-induced lung injury
Bibliographic record
Abstract
INTRODUCTION: Endothelial cell injury is an important component of acute lung injury. Platelet-endothelial cell adhesion molecule-1 (PECAM1) is a transmembrane protein that connects endothelial cells to one another and can be detected as a soluble, truncated protein (sPECAM1) in serum. We hypothesized that injurious mechanical ventilation (MV) leads to shedding of PECAM1 from lung endothelial cells resulting in increasing sPECAM1 levels in the systemic circulation. METHODS: We studied 36 Sprague-Dawley rats in two prospective, randomized, controlled studies (healthy and septic) using established animal models of ventilator-induced lung injury. Animals (n = 6 in each group) were randomized to spontaneous breathing or two MV strategies: low tidal volume (VT) (6 ml/kg) and high-VT (20 ml/kg) on 2 cmH2O of positive end-expiratory pressure (PEEP). In low-VT septic animals, 10 cmH2O of PEEP was applied. We performed pulmonary histological and physiological evaluation and measured lung PECAM1 protein content and serum sPECAM1 levels after four hours ventilation period. RESULTS: High-VT MV caused severe lung injury in healthy and septic animals, and decreased lung PECAM1 protein content (P < 0.001). Animals on high-VT had a four- to six-fold increase of mean sPECAM1 serum levels than the unventilated counterpart (35.4 ± 10.4 versus 5.6 ± 1.7 ng/ml in healthy rats; 156.8 ± 47.6 versus 35.6 ± 12.6 ng/ml in septic rats) (P < 0.0001). Low-VT MV prevented these changes. Levels of sPECAM1 in healthy animals on high-VT MV paralleled the sPECAM1 levels of non-ventilated septic animals. CONCLUSIONS: Our findings suggest that circulating sPECAM1 may represent a promising biomarker for the detection and monitoring of ventilator-induced lung injury.
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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 | 0.000 |
| 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 | 0.000 | 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".