Engineered Hemostatic Biomaterials for Sealing Wounds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 it