Manufacturing method affects mitochondrial <scp>DNA</scp> release and extracellular vesicle composition in stored red blood cells
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Damage-associated molecular patterns (DAMPs) are found in transfusion products, but their potential impacts are not fully understood. We examined the influence of manufacturing method on levels of mitochondrial (mt) DNA and extracellular vesicle (EV) DAMPs in red cell concentrates (RCCs). MATERIALS AND METHODS: Eighty-seven RCCs were prepared using nine different methods (6-15 units/method), including three apheresis, five whole blood (WB)-derived leucoreduced (LR) and one WB-derived non-LR method. On storage days 5 and 42, levels of mtDNA (by PCR) and number and cell of origin of EVs (by flow cytometry) were assessed in RCC supernatants. RESULTS: There was a 100-fold difference in mtDNA levels among methods, with highest levels in non-LR, followed by MCS+ and Trima apheresis RCCs. There was a 10-fold difference in EV levels among methods. RBC-derived CD235a+ EVs were found in fresh RCCs and increased in most during storage. Platelet-derived CD41a+ EVs were highest in non-LR and Trima RCCs and did not change during storage. WBC-derived EVs were low in most RCCs; CD14+ EVs increased in several RCCs during storage. CONCLUSION: DAMPs in RCCs vary by manufacturing method. MtDNA and EV could be informative quality markers that may be relevant to RCC immunomodulatory potential.
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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".