Encapsulation of fibrinogen and thrombin with calcium carbonate for hemorrhage control
Bibliographic record
Abstract
Hemorrhage remains the major cause of death in combat and civilian trauma, although significant advances in hemostatic agents and blood products have enhanced damage control resuscitation and reduced mortality. Currently, few hemostats are available for cessation of non-compressible torso hemorrhage, e.g., intra-abdominal hemorrhage. To address this hard-to-solve problem, self-propelling hemostatic particles composed of coagulation factors e.g., fibrinogen, thrombin, CaCO3 and protonated tranexamic acid (TXA+) that can move against blood flow and promote clot formation have been developed. This paper describes the preparation and characterization of CaCO3-encapsulated fibrinogen/thrombin particles. The particles were prepared by interfacial reaction method using water-oil-water emulsion under different conditions that varied in the concentrations of the coagulation factor and ammonium carbonate solutions, amounts of surfactants, mixing speed, volume ratio between water and oil phases. The resulting CaCO3 encapsulated fibrinogen/thrombin particles were characterized via light microscopy for morphology, gel electrophoresis for presence of fibrinogen and thrombin, rotational thromboelastometry for hemostatic effects and reaction with TXA+ for self-propulsion test. It was found that CaCO3 particles had spherical structure with less than 10 µm in diameter, could encapsulate fibrinogen and thrombin, enhance blood coagulation, and generate bubbles for propulsion by reacting with TXA+. Moreover, when particles were combined with TXA+, a synergistic hemostatic effect was obtained. These hemostatic and self-propelling properties could be optimized via changes to preparation method and composition. Further studies in animal bleeding models 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) | 0.000 | 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".