Extracellular vesicles: effectors of transfusion-related acute lung injury
Bibliographic record
Abstract
Respiratory transfusion reactions represent some of the most severe adverse reactions related to receiving blood products. Of those, transfusion-related acute lung injury (TRALI) is associated with elevated morbidity and mortality. TRALI is characterized by severe lung injury associated with inflammation, pulmonary neutrophil infiltration, lung barrier leak, and increased interstitial and airspace edema that cause respiratory failure. Presently, there are few means of detecting TRALI beyond clinical definitions based on physical examination and vital signs or preventing/treating TRALI beyond supportive care with oxygen and positive pressure ventilation. Mechanistically, TRALI is thought to be mediated by the culmination of two successive proinflammatory hits, which typically comprise a recipient factor (1st hit—e.g., systemic inflammatory conditions) and a donor factor (2nd hit—e.g., blood products containing pathogenic antibodies or bioactive lipids). An emerging concept in TRALI research is the contribution of extracellular vesicles (EVs) in mediating the first and/or second hit in TRALI. EVs are small, subcellular, membrane-bound vesicles that circulate in donor and recipient blood. Injurious EVs may be released by immune or vascular cells during inflammation, by infectious bacteria, or in blood products during storage, and can target the lung upon systemic dissemination. This review assesses emerging concepts such as how EVs: 1) mediate TRALI, 2) represent targets for therapeutic intervention to prevent or treat TRALI, and 3) serve as biochemical biomarkers facilitating TRALI diagnosis and detection in at-risk patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".