Investigation on the Influence of Protein Corona and Platelet Adhesion on Storage Bag Surface on the Platelet Storage Lesion
Bibliographic record
Abstract
Platelet transfusion is an indispensable therapy used in contexts ranging from hemorrhagic bleeding to chemotherapy. However, platelet shelf life and quality deteriorate during storage in a process termed the platelet storage lesion (PSL), resulting in chronic shortages globally. The PSL is thought to be partially attributed to the bio-incompatible polyvinyl chloride (PVC) storage bags that promote protein fouling and platelet adhesion. Developing platelet-friendly materials is therefore essential for improving platelet storage quality. This study aimed to delineate the contributions of protein adsorption and platelet adhesion to the progression of the PSL. Hydrophilic coatings are screened to identify those which resist protein and platelet adhesion most effectively. These coatings are translated into mini-platelet bags and tested in long-term standard blood banking conditions. Significant reductions in platelet adhesion after 7-day storage do not affect platelet quality. Proteomic characterization of the bag surfaces revealed dynamic changes in the protein coronas on coated and uncoated bag over time, but are uncorrelated with platelet quality. This research demonstrates a method to identify platelet-compatible coatings suitable for long-term storage, as well as a novel approach to characterize the surface of blood storage bags.
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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 | 0.000 |
| 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 | 0.000 | 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".