Engineered Hemostatic Biomaterials for Sealing Wounds
Bibliographic record
Abstract
Hemostatic biomaterials show great promise in wound control for the treatment of uncontrolled bleeding associated with damaged tissues, traumatic wounds, and surgical incisions. A surge of interest has been directed at boosting hemostatic properties of bioactive materials via mechanisms triggering the coagulation cascade. A wide variety of biocompatible and biodegradable materials has been applied to the design of hemostatic platforms for rapid blood coagulation. Recent trends in the design of hemostatic agents emphasize chemical conjugation of charged moieties to biomacromolecules, physical incorporation of blood-coagulating agents in biomaterials systems, and superabsorbing materials in either dry (foams) or wet (hydrogel) states. In addition, tough bioadhesives are emerging for efficient and physical sealing of incisions. In this Review, we highlight the biomacromolecular design approaches adopted to develop hemostatic bioactive materials. We discuss the mechanistic pathways of hemostasis along with the current standard experimental procedures for characterization of the hemostasis efficacy. Finally, we discuss the potential for clinical translation of hemostatic technologies, future trends, and research opportunities for the development of next-generation surgical materials with hemostatic properties for wound management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".