TPU-based antiplatelet cardiovascular prostheses prepared using fused deposition modelling
Bibliographic record
Abstract
This work describes the use of fused deposition modelling (FDM) to prepare antiplatelet thermoplastic polyurethane (TPU)-based tubular grafts. FDM 3D-printing technology is widely available and provides the ability to easily design tubular grafts on demand, enabling the customisation of vascular prosthesis dimensions. An antiplatelet drug, dipyridamole (DIP), was combined with TPU using hot-melt extrusion to prepare filaments. DIP cargos ranged between 5 and 20% (w/w). The resulting filaments were used to prepare small diameter vascular grafts using FDM. These grafts were characterised. Moreover, DIP release kinetics, antiplatelet activity and in vitro hemo- and cytocompatibility were evaluated. The results suggested that the materials could provide sustained DIP release for 30 days. Moreover, the presence of 5% DIP in the material showed a clear antiplatelet effect compared with pristine TPU. Alternatively, higher DIP loadings resulted higher surface roughness leading to higher platelet adhesion. Therefore, the biocompatibility of 5% DIP samples was tested showing that this type of materials allowed higher HUVEC cell proliferation compared to pristine TPU samples. Finally, DIP loaded TPU was combined with rifampicin-loaded TPU to prepare double-layered tubular grafts. These grafts demonstrated a clear antimicrobial activity against both Staphylococcus aureus and Escherichia coli.
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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 | 0.001 | 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".