Assessment of the soluble proteins <scp>HMGB1</scp>, <scp>CD40L</scp> and <scp>CD62P</scp> during various platelet preparation processes and the storage of platelet concentrates: The <scp>BEST</scp> collaborative study
Bibliographic record
Abstract
BACKGROUND: Structural and biochemical changes in stored platelets are influenced by collection and processing methods. This international study investigates the effects of platelet (PLT) processing and storage conditions on HMGB1, sCD40L, and sCD62P protein levels in platelet concentrate supernatants (PCs). STUDY DESIGN/METHODS: PC supernatants (n = 3748) were collected by each international centre using identical centrifugation methods (n = 9) and tested centrally using the ELISA/Luminex platform. Apheresis versus the buffy coat (BC-PC) method, plasma storage versus PAS and RT storage versus cold (4°C) were investigated. We focused on PC preparation collecting samples during early (RT: day 1-3; cold: day 1-5) and late (RT: day 4-7; cold: day 7-10) storage time points. RESULTS: HMGB1, sCD40L, and sCD62P concentrations were similar during early storage periods, regardless of storage solution (BC-PC plasma and BC-PC PAS-E) or temperature. During storage and without PAS, sCD40L and CD62P in BC-PC supernatants increased significantly (+33% and +41%, respectively) depending on storage temperature (22 vs. 4°C). However, without PAS-E, levels decreased significantly (-31% and -20%, respectively), depending on storage temperature (22 vs. 4°C). Contrastingly, the processing method appeared to have greater impact on HMGB1 release versus storage duration. These data highlight increases in these parameters during storage and differences between preparation methods and storage temperatures. CONCLUSIONS: The HMGB1 release mechanism/intracellular pathways appear to differ from sCD62P and sCD40L. The extent to which these differences affect patient outcomes, particularly post-transfusion platelet increment and adverse events, warrants further investigation in clinical trials with various therapeutic indications.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".