Production of viable and functional neutrophils in granulocyte concentrates with the Reveos automated system
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Granulocyte transfusions may benefit patients with neutropaenia and life-threatening infections unresponsive to antimicrobial therapies. Current aphaeresis-based granulocyte concentrate (GC) production requires donor stimulation and hydroxyethyl starch (HES), which raises safety and supply concerns. This study assessed the feasibility and quality of GCs derived from pooling 10 residual leukocyte units (RLUs) processed via the Reveos automated blood processing system. MATERIALS AND METHODS: Whole blood (WB) from 10 ABO-compatible donors was processed using the Reveos system to obtain 10 mL RLUs. A modified platelet pooling device enabled sterile pooling of RLUs with added plasma. The final product was irradiated and analysed on days 0, 1 and 2 post-irradiation. Parameters assessed included cell counts, sterility, biochemical properties, viability, surface markers (CD15, CD10, CD62L and CD11b) and neutrophil functions: chemotaxis, phagocytosis, oxidative burst and H₂O₂ release. RESULTS: ) and remained viable on day 2. Functional assays demonstrated sustained phagocytic and respiratory activity up to 48 h post-processing, although chemotactic response and reactive oxygen species (ROS) production declined significantly from 24 h after processing (p < 0.05). CONCLUSION: Pooling of Reveos-derived RLUs is a feasible, HES-free strategy to produce viable and functional GCs over 24 h from processing and irradiation. This approach provides a readily available alternative to aphaeresis products that could potentially enhance transfusion coordination.
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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) | 0.000 | 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".