Neutrophils and their Fc receptors are essential in a mouse model of transfusion-related acute lung injury
Bibliographic record
Abstract
Transfusion-related acute lung injury (TRALI) is the most common cause of transfusion-related mortality. To explore the pathogenesis of TRALI, we developed an in vivo mouse model based on the passive transfusion of an MHC class I (MHC I) mAb (H2Kd) to mice with the cognate antigen. Transfusion of the MHC I mAb to BALB/c mice produced acute lung injury with increased excess lung water, increased lung vascular and lung epithelial permeability to protein, and decreased alveolar fluid clearance. There was 50% mortality at a 2-hour time point after Ab administration. Pulmonary histology and immunohistochemistry revealed prominent neutrophil sequestration in the lung microvasculature that occurred concomitantly with acute peripheral blood neutropenia, all within 2 hours of administration of the mAb. Depletion of neutrophils by injection of anti-granulocyte mAb Gr-1 protected mice from lung injury following MHC I mAb challenge. FcRgamma-/- mice were resistant to MHC I mAb-induced lung injury, while adoptive transfer of wild-type neutrophils into the FcRgamma-/- animals restored lung injury following MHC I mAb challenge. In conclusion, in a clinically relevant in vivo mouse model of TRALI using an MHC I mAb, the mechanism of lung injury was dependent on neutrophils and their Fc gamma receptors.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".