Bacterial-Nanocellulose-Based Biointerfaces and Biomimetic Constructs for Blood-Contacting Medical Applications
Bibliographic record
Abstract
Understanding the interaction between biomaterials and blood is critical in the design of novel biomaterials for use in biomedical applications. Depending on the application, biomaterials can be designed to promote hemostasis, slow or stop bleeding in an internal or external wound, or prevent thrombosis for use in permanent or temporary medical implants. Bacterial nanocellulose (BNC) is a natural, biocompatible biopolymer that has recently gained interest for its potential use in blood-contacting biomedical applications (e.g., artificial vascular grafts), due to its high porosity, shapeability, and tissue-like properties. To promote hemostasis, BNC has been modified through oxidation or functionalization with various peptides, proteins, polysaccharides, and minerals that interact with the coagulation cascade. For use as an artificial vascular graft or to promote vascularization, BNC has been extensively researched, with studies investigating different modification techniques to enhance endothelialization such as functionalizing with adhesion peptides or extracellular matrix (ECM) proteins as well as tuning the structural properties of BNC such as surface roughness, pore size, and fiber size. While BNC inherently exhibits comparable mechanical characteristics to endogenous blood vessels, these mechanical properties can be enhanced through chemical functionalization or through altering the fabrication method. In this review, we provide a comprehensive overview of the various modification techniques that have been implemented to enhance the suitability of BNC for blood-contacting biomedical applications and different testing techniques that can be applied to evaluate their performance. Initially, we focused on the modification techniques that have been applied to BNC for hemostatic applications. Subsequently, we outline the different methods used for the production of BNC-based artificial vascular grafts and to generate vasculature in tissue engineered constructs. This sequential organization enables a clear and concise discussion of the various modifications of BNC for different blood-contacting biomedical applications and highlights the diverse and versatile nature of BNC as a natural biomaterial.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".