Red‐blood‐cell manufacturing methods and storage solutions differentially induce pulmonary cell activation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Red-blood-cell (RBC) transfusion is associated with lung injury, which is further exacerbated by mechanical ventilation. Manufacturing methods of blood products differ globally and may play a role in the induction of pulmonary cell activation through alteration of the immunomodulatory property of the products. Here, the effect of different manufacturing methods on pulmonary cell activation was investigated in an in vitro model of mechanical ventilation. MATERIALS AND METHODS: Pulmonary type II cells were incubated with supernatant from fresh and old RBC products obtained via whole blood filtration (WBF), red cell filtration (RCF), apheresis-derived (AD) or whole blood-derived (WBD) methods. Lung cells were subjected to 25% stretch for 24 h. Controls were non-stretched or non-incubated cells. RESULTS: Fresh but not old AD products and WBF products induce lung cell production of pro-inflammatory cytokines and chemokines, which was not observed with WBD or RCF products. Effects were associated with an increased amount of platelet-derived vesicles and an increased thrombin-generating capacity. Mechanical stretching of lung cells induced more severe cell injury compared to un-stretched controls, including alterations in the cytoskeleton, which was further augmented by incubation with AD products. In all read-out parameters, RCF products seemed to induce less injury compared to the other products. CONCLUSIONS: Our findings show that manufacturing methods of RBC products impact pulmonary cell activation, which may be mediated by the generation of vesicles in the product. We suggest RBC manufacturing method may be an important factor in understanding the association between RBC transfusion and lung injury.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".