The polyvinyl alcohol–bacterial cellulose system as a new nanocomposite for biomedical applications
Bibliographic record
Abstract
Finding materials suitable for soft tissue replacement is an important aspect for medical devices design and fabrication. There is a need to develop a material that will not only display similar mechanical properties as the tissue it is replacing, but also shows improved life span, biocompatibility, nonthrombogenic, and low degree of calcification. Polyvinyl alcohol (PVA) is a hydrophilic biocompatible polymer with various characteristics desired for biomedical applications. PVA can be transformed into a solid hydrogel with good mechanical properties by physical crosslinking, using freeze-thaw cycles. Hydrophilic bacterial cellulose (BC) fibers of an average diameter of 50 nm are produced by the bacterium Acetobacter xylinum, using a fermentation process. They are used in combination with PVA to form biocompatible nanocomposites. The resulting nanocomposites possess a broad range of mechanical properties and can be made with mechanical properties similar to that of cardiovascular tissues, such as aorta and heart valve leaflets. The stress-strain properties for porcine aorta are matched by at least one type of PVA-BC nanocomposite in both the circumferential and the axial tissue directions. A PVA-BC nanocomposite with similar properties as heart valve tissue is also developed. Relaxation properties of all samples, which are important for cardiovascular applications, were also studied and found to relax at a faster rate and to a lower residual stress than the tissues they might replace. The new PVA-BC composite is a promising material for cardiovascular soft tissue replacement applications.
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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) | 0.001 | 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".