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Record W2791608704 · doi:10.1097/ccm.0000000000002989

Targeting Transfusion-Related Acute Lung Injury: The Journey From Basic Science to Novel Therapies

2018· review· en· W2791608704 on OpenAlexafffund
John W. Semple, Mark J. McVey, Michael Kim, Johan Rebetz, Wolfgang M. Kuebler, Rick Kapur

Bibliographic record

VenueCritical Care Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsSickKids FoundationCanadian Blood ServicesUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchHealth CanadaVetenskapsrådetCanadian Blood Services
KeywordsTransfusion-related acute lung injuryMedicinePathogenesisDiffuse alveolar damageIntensive care medicineBlood transfusionLungImmunologyAcute respiratory distressInternal medicinePulmonary edema

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.044
GPT teacher head0.382
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations60
Published2018
Admission routes2
Has abstractyes

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