Ex vivo hemostatic and immuno‐inflammatory profiles of freeze‐dried plasma
Bibliographic record
Abstract
BACKGROUND: Hemorrhage is a leading cause of preventable death in civilian and military trauma. Freeze-dried plasma is promising for hemostatic resuscitation in remote prehospital settings, given its potential benefits in reducing blood loss and mortality, long storage at ambient temperatures, high portability, and rapid reconstitution for transfusion in austere environments. Here we assess the ex vivo characteristics of a novel Terumo's freeze-dried plasma product (TFDP). STUDY DESIGN AND METHODS: Rotational thromboelastometry (ROTEM) tests (INTEM, EXTEM, and FIBTEM) were conducted on plasma samples at 37°C with a ROTEM delta-machine using standard reagents and procedures. The following samples were analyzed: pooled plasma to produce TFDP, TFDP reconstituted, and stored immediately at -80°C, reconstituted TFDP stored at 4°C for 24 h and room temperature (RT) for 4 h before freezing at -80°C. Analysis of plasma concentrations of selected cytokines, chemokines, and vascular molecules was performed using a multiplex immunoassay system. One-way ANOVA with post hoc tests assessed differences in hemostatic and inflammatory properties. RESULTS: No significant differences in ROTEM variables (coagulation time [CT], clot formation time, α-angle, maximum clot firmness, and lysis index 30) between the TFDP-producing plasma and reconstituted TFDP samples were observed. Compared to control plasma, reconstituted TFDP stored at 4°C for 24 h or RT for 4 h showed a longer INTEM CT. Levels of immuno-inflammatory mediators were similar between frozen plasma and TFDP. CONCLUSIONS: TFDP is equivalent to frozen plasma with respect to global hemostatic and immuno-inflammatory mediator profiles. Further investigations of TFDP in trauma-induced coagulopathy models and bleeding patients are warranted.
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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".