Targeting Transfusion-Related Acute Lung Injury: The Journey From Basic Science to Novel Therapies
Bibliographic record
Abstract
OBJECTIVES: Transfusion-related acute lung injury is characterized by the onset of respiratory distress and acute lung injury following blood transfusion, but its pathogenesis remains poorly understood. Generally, a two-hit model is presumed to underlie transfusion-related acute lung injury with the first hit being risk factors present in the transfused patient (such as inflammation), whereas the second hit is conveyed by factors in the transfused donor blood (such as antileukocyte antibodies). At least 80% of transfusion-related acute lung injury cases are related to the presence of donor antibodies such as antihuman leukocyte or antihuman neutrophil antibodies. The remaining cases may be related to nonantibody-mediated factors such as biolipids or components related to storage and ageing of the transfused blood cells. At present, transfusion-related acute lung injury is the leading cause of transfusion-related fatalities and no specific therapy is clinically available. In this article, we critically appraise and discuss recent preclinical (bench) insights related to transfusion-related acute lung injury pathogenesis and their therapeutic potential for future use at the patients' bedside in order to combat this devastating and possibly fatal complication of transfusion. DATA SOURCES: We searched the PubMed database (until August 22, 2017). STUDY SELECTION: Using terms: "Transfusion-related acute lung injury," "TRALI," "TRALI and therapy," "TRALI pathogenesis." DATA EXTRACTION: English-written articles focusing on transfusion-related acute lung injury pathogenesis, with potential therapeutic implications, were extracted. DATA SYNTHESIS: We have identified potential therapeutic approaches based on the literature. CONCLUSIONS: We propose that the most promising therapeutic strategies to explore are interleukin-10 therapy, down-modulating C-reactive protein levels, targeting reactive oxygen species, or blocking the interleukin-8 receptors; all focused on the transfused recipient. In the long-run, it may perhaps also be advantageous to explore other strategies aimed at the transfused recipient or aimed toward the blood product, but these will require more validation and confirmation first.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".