Buffy‐coat platelet variables and metabolism during storage in additive solutions or plasma
Bibliographic record
Abstract
BACKGROUND: Buffy-coat processing allows for the use of platelet additive solutions (PASs). PASs reduce plasma-associated transfusion reactions and conserve plasma for transfusion or fractionation. Platelet (PLT) storage in plasma was compared to storage in three commercially available PASs compared to assess their influence on in vitro laboratory variables. STUDY DESIGN AND METHODS: Platelet concentrates (PCs) were prepared from leukoreduced pools of four buffy coats (BCPs) suspended in autologous plasma or one of PASs (Composol, Fresenius-Kabi; T-Sol, Baxter Corp.; or SSP+, MacoPharma). On Days 1, 2, 3, 5, and 7 of storage, samples were tested for PLT concentration, mean PLT volume (MPV), CD62P, morphology, pO2, pCO2, glucose, lactate and total protein concentration, pH, extent of shape change (ESC), and hypotonic shock response (HSR). Data were analyzed by analysis of variance (ANOVA) with repeated measures and t tests. RESULTS: PLT recoveries from BCPs were higher (p < 0.05) with plasma than any PAS. Storage medium and duration did not affect PLT concentration or MPV over time. CD62P expression and morphology were significantly different among PCs pooled with different media. ANOVA showed (p < 0.05) differences among the rates of change of pCO2, pH, glucose consumption, lactate production, and ESC; PASs such as Composol and SSP+ offered excellent maintenance of pH and low rates of glucose consumption. PAS performed poorly in ESC and HSR compared to plasma. Correlation studies reveal far more significant correlations between variables of PLTs in PAS than in plasma. CONCLUSION: Newer PASs, for example, SSP+ and Composol, can maintain PLT integrity and moderate metabolism similarly to plasma but offer consistently lower PLT recoveries and limited osmotic balance.
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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 | 0.000 | 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) | 0.002 | 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".