Bioactive lipids as biomarkers of adverse reactions associated with apheresis platelet concentrate transfusion
Bibliographic record
Abstract
Platelet concentrate (PC) transfusion seeks to provide haemostasis in patients presenting severe central thrombocytopenia or severe bleeding. PCs may induce adverse reactions (AR) that can occasionally be severe (SAR). PCs contain active biomolecules such as cytokines and lipid mediators. The processing and storage of PCs creates so-called structural and biochemical storage lesions that accumulate when blood products reach their shelf life. We sought to investigate lipid mediators as bioactive molecules of interest during storage and review associations with adverse reactions post-transfusion. To facilitate understanding, we focused on single donor apheresis (SDA) PCs with approximately 31.8% of PCs being delivered in our setting. Indeed, pooled PCs are the most widely transfused products, but the study of a single donor lipid mediator is easier to interpret. We are investigating key lipid mediators involved in AR. Adverse reactions were closely monitored in accordance with current national and regional haemovigilance protocols. Residual PCs were analysed post-transfusion in a series of observations, both with and without severe reactions in recipients. A decrease in the lysophosphatidylcholine species to produce the lysophosphatidic acid species has been observed during storage and in the case of AR. Lysophosphatidic acid increased with primarily platelet-inhibitor lipids. Anti-inflammatory platelet-induced inhibition lipids were weakly expressed in cases of severe adverse reactions. We therefore propose that a decrease in lysophosphatidylcholine and an increase in lysophosphatidic acid can prospectively predict serious adverse transfusion reactions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".