Emulsion-Based Encapsulation of Fibrinogen with Calcium Carbonate for Hemorrhage Control
Bibliographic record
Abstract
Hemorrhage, particularly non-compressible torso bleeding, remains the leading cause of preventable death in trauma. Self-propelling hemostats composed of thrombin-calcium carbonate (CaCO3) particles and protonated tranexamic acid (TXA+) have been shown to reduce blood loss and mortality in severe bleeding animal models. To further enhance both hemostatic and self-propelling properties, this study was to investigate fibrinogen-CaCO3 particles prepared via a water-oil-water (W/O/W) emulsion method. The particles were characterized using light and fluorescence microscopy, gel electrophoresis, rotational thromboelastometry (ROTEM), and video motion tracking. The method produced spherical micrometer-sized particles with various yields and fibrinogen content, depending on the preparation conditions. The highest yield was achieved with sodium carbonate (SC), followed by ammonium carbonate (AC) and sodium bicarbonate (SBC). AC and paraffin generated smaller particles compared to SC and heptane, which were used as the carbonate source and oil phase, respectively. Fibrinogen incorporation led to an increase in particle size, indicating a correlation between fibrinogen content and particle size. Fluorescence microscopy confirmed successful fibrinogen encapsulation, with various amounts and hemostatic effects as assessed by gel electrophoresis and ROTEM. Combining fibrinogen-CaCO3 particles with TXA+ and thrombin-CaCO3 particles showed synergistic hemostatic effects. All fibrinogen-encapsulated particles exhibited self-propulsion when mixed with TXA+ and exposed to water, regardless of fibrinogen content. This study advances current hemostatic particle technology by demonstrating enhanced self-propulsion and fibrinogen incorporation via the W/O/W emulsion method. Further optimization of the encapsulation method could enhance the effectiveness of fibrinogen-CaCO3 particles for hemorrhage control.
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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".