Freeze‐dried plasma: From damage control resuscitation to coronavirus disease 2019 therapy
Bibliographic record
Abstract
BACKGROUND: Freeze-dried plasma (FDP) is a promising blood component for prehospital resuscitation given its logistic advantages over fresh frozen plasma (FFP). COVID-19 convalescent (CC) plasma has been used to treat coronavirus disease 2019 (COVID-19) patients, and its corresponding FDP has potential use during future pandemics. Therefore, we conducted the study to determine if the hemostatic and immunological properties of plasma can be retained after lyophilization. STUDY DESIGN AND METHODS: Hemostatic tests were conducted with Rotational Thromboelastometry (ROTEM) and a Stago analyzer. Anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG (Immunoglobulin G) and neutralizing activity were analyzed using Meso Scale Diagnostics immunoassay kits. RESULTS: There were no differences in ROTEM parameters and Stago measurements for prothrombin time (PT), partial thromboplastin time (PTT), fibrinogen and D-dimer concentrations, and antithrombin, factor V, VIII, and protein S activities between FFP and FDP for either pre-COVID-19 or CC samples. Differences were observed in INTEM clotting time and PT and PTT when comparing reconstituted FDP stored at 4°C for 24 h or room temperature for 4 h to healthy control. Both CC-FFP and CC-FDP showed two orders of magnitude higher concentrations of IgG antibodies against SARS-CoV-2 antigens than pre-COVID-19-FFP and pre-COVID-19-FDP and healthy control. Similarly, the CC samples showed approximately 4-fold higher %-inhibition of receptor binding than the pre-COVID-19 samples. There were no differences in either the antibody levels or neutralization activity between CC-FFP and CC-FDP. DISCUSSION: We demonstrated that FDP and CC-FDP retained the same hemostatic and antibody functional activities relative to their initial plasma sources, supporting clinical evaluation of their benefits in severe trauma and COVID-19 patients.
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 | 0.001 | 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 | 0.001 |
| 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".