Platelet‐derived extracellular vesicles convey mitochondrial DAMPs in platelet concentrates and their levels are associated with adverse reactions
Bibliographic record
Abstract
BACKGROUND Whereas platelet transfusion is a common medical procedure, inflammation still occurs in a fraction of transfused individuals despite the absence of any apparent infectious agents. Platelets can shed membrane vesicles, called extracellular vesicles (EVs), some of which contain mitochondria (mito+EV). With its content of damage‐associated molecular pattern (DAMP), the mitochondrion can stimulate the innate immune system. Mitochondrial DNA (mtDNA) is a recognized DAMP detected in the extracellular milieu in numerous inflammatory conditions and in platelet concentrates. We hypothesized that platelet‐derived mitochondria encapsulated in EVs may represent a reservoir of mtDNA. STUDY DESIGN AND METHODS Herein, we explored the implication of mito+EVs in the occurrence of mtDNA quantified in platelet concentrate supernatants that induced or did not induce transfusion adverse reactions. RESULTS We observed that EVs were abundant in platelet concentrates, and platelet‐derived mito+EVs were more abundant in platelet concentrates that induced adverse reactions. A significant correlation (rs = 0.73; p < 0.0001) between platelet‐derived mito+EV levels and mtDNA concentrations was found. However, there was a nonsignificant correlation between the levels of EVs without mitochondria and mtDNA concentrations (rs = −0.11; p = 0.5112). The majority of the mtDNA was encapsulated into EVs. CONCLUSION This study suggests that platelet‐derived EVs, such as those that convey mitochondrial DAMPs, may be a useful biomarker for the prediction of potential risk of adverse transfusion reactions. Moreover, our work implies that investigations are necessary to determine whether there is a causal pathogenic role of mitochondrial DAMP encapsulated in EVs as opposed to mtDNA in solution.
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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) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".