Acid sphingomyelinase mediates murine acute lung injury following transfusion of aged platelets
Bibliographic record
Abstract
Pulmonary complications from stored blood products are the leading cause of mortality related to transfusion. Transfusion-related acute lung injury is mediated by antibodies or bioactive mediators, yet underlying mechanisms are incompletely understood. Sphingolipids such as ceramide regulate lung injury, and their composition changes as a function of time in stored blood. Here, we tested the hypothesis that aged platelets may induce lung injury via a sphingolipid-mediated mechanism. To assess this hypothesis, a two-hit mouse model was devised. Recipient mice were treated with 2 mg/kg intraperitoneal lipopolysaccharide (priming) 2 h before transfusion of 10 ml/kg stored (1-5 days) platelets treated with or without addition of acid sphingomyelinase inhibitor ARC39 or platelets from acid sphingomyelinase-deficient mice, which both reduce ceramide formation. Transfused mice were examined for signs of pulmonary neutrophil accumulation, endothelial barrier dysfunction, and histological evidence of lung injury. Sphingolipid profiles in stored platelets were analyzed by mass spectrophotometry. Transfusion of aged platelets into primed mice induced characteristic features of lung injury, which increased in severity as a function of storage time. Ceramide accumulated in platelets during storage, but this was attenuated by ARC39 or in acid sphingomyelinase-deficient platelets. Compared with wild-type platelets, transfusion of ARC39-treated or acid sphingomyelinase-deficient aged platelets alleviated lung injury. Aged platelets elicit lung injury in primed recipient mice, which can be alleviated by pharmacological inhibition or genetic deletion of acid sphingomyelinase. Interventions targeting sphingolipid formation represent a promising strategy to increase the safety and longevity of stored blood products.
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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) | 0.001 | 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 | 0.001 | 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".