Novel system for storage of buffy‐coat‐derived platelet concentrates in a glucose‐based platelet additive solution: parameters and metabolism during storage and comparison to plasma
Bibliographic record
Abstract
BACKGROUND: In Europe, buffy-coat processing allows for the use of platelet additive solutions (PAS). These solutions, however, have long been questioned for their lack of glucose, a potentially essential nutrient for platelet storage. Using a novel, practical, two-part system for incorporation of glucose into an additive solution (PAS-G), this study compares platelet storage in plasma to storage in PAS-G. STUDY DESIGN AND METHODS: A paired study design of platelet concentrates (PC) were prepared from leucoreduced pools of eight buffy coats (BCP) split into two equal pools, with suspension in autologous plasma, or PAS-G. On days 2, 5, 7 and 9 of storage, samples were tested using standard in vitro platelet parameters. Data were analysed by paired Student's t-tests. RESULTS: During storage, PCs in PAS-G maintain a quality profile that is strikingly similar to PCs stored in plasma in terms of platelet activation (CD62) morphology score, swirl, glucose metabolism and pH. However, PCs in PAS-G perform lower (P < 0.05) in the %ESC and %HSR assays. CONCLUSION: PAS-G's novel presentation allows incorporation of glucose into the additive solution so that it is roughly equivalent to plasma for the maintenance of buffy-coat PCs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".