Reconstituted cryopreserved platelets synthesize proteins during short‐term storage and packaging a defined subset into microvesicles
Bibliographic record
Abstract
BACKGROUND: Cryopreservation of platelets (PLTs) could allow extension of their shelf-life to years, compared to days for liquid stored platelets. Due to their greater hemostatic effect, reconstituted cryopreserved platelets (cryo-PLTs) would be able to support bleeding emergencies. Since protein synthesis has been linked to PLT functions, such as clot formation and immune responses, the translational capacity of reconstituted cryo-PLTs was assessed upon thawing and short-term storage. METHODS/MATERIALS: Platelets were frozen at -80°C with 5-6% DMSO. Upon thawing, they were reconstituted in plasma and then aliquoted (12 ml) into mini-bags and assessed over 24 h of storage at RT. One series served as control; the second and third series were spiked with either 300 μM puromycin (Pm) or 227 nM biotin-labeled Pm. Samples were tested for in vitro quality and PLT microvesicle enumeration by flow cytometry. Protein synthesis in cryo-PLTs was assessed using a modified method based on puromycin-associated nascent chain proteomics. RESULTS: In vitro parameters of reconstituted and subsequently stored platelets were consistent with previously published results. Mass-spectrometry analyses identified that 22 proteins were synthesized in PLTs and 13 of those were observed in platelet microvesicles (PMVs). CONCLUSION: Cryo-PLTs can synthesize proteins upon reconstitution and storage. Discovery of a subset of these proteins in the PMV suggests a role in vesicle encapsulation, possibly in a selective manner. This observation provides novel insights into the capacity for protein synthesis in cryo-PLTs and the potential regulation of protein packaging into PMV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".