Developing Specific Therapeutic Strategies for Transfusion-Related Acute Lung Injury. An Overview of Potentially Useful Animal Models
Bibliographic record
Abstract
Transfusion-related (TR)- acute lung injury (ALI) is the leading cause of transfusion-related morbidity and mortality. The pathogenesis of TRALI is thought to be a "two hit"-entity: the "first hit" is (any) proinflammatory pulmonary condition (e.g., pneumonia, sepsis or lung contusion) resulting in activation of lung endothelium with sequestration of polymorphonuclear neutrophils - the "second hit" is provided by transfusion of a blood product. Either antibodies against neutrophils are thought to be implicated in the activation of the sequestrated neutrophils, or bioactive lipids (which accumulate during storage of blood products) induce the "second hit", finally resulting in lung injury. Preventive measures do not prevent all TRALI cases. Also, TRALI is most probably underdiagnosed. In this review, we call for the development of therapeutic approaches for this potentially life-threatening disease. Several interventions which are beneficial in ALI and may also be beneficial in TRALI are discussed. The application of these interventions requires the development of clinically relevant TRALI animal models. We discuss the present TRALI animal models and their shortcomings and propose future animal models, in which clinically relevant "first hits" can be applied, thereby imitating the complex clinical situation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".