Multifunctional 3D‐Printed Patches for Long‐Term Drug Release Therapies after Myocardial Infarction
Bibliographic record
Abstract
Abstract A biomaterial system incorporating nanocellulose, poly(glycerol sebacate), and polypyrrole is introduced for the treatment of myocardial infarction. Direct ink writing of the multicomponent aqueous suspensions allows multifunctional lattice structures that not only feature elasticity and electrical conductivity but enable cell growth. They are proposed as cardiac patches given their biocompatibility with H9c2 cardiomyoblasts, which attach extensively at the microstructural level, and induce their proliferation for 28 days. Two model drugs (3i‐1000 and curcumin) are investigated for their integration in the patches, either by loading in the precursor suspension used for extrusion or by direct impregnation of the as‐obtained, dry lattice. In studies of drug release conducted for five months, a slow in vitro degradation of the cardiac patches is observed, which prevents drug burst release and indicates their suitability for long‐term therapy. The combination of biocompatibility, biodegradability, mechanical strength, flexibility, and electrical conductivity fulfills the requirement of the highly dynamic and functional electroresponsive cardiac tissue. Overall, the proposed cardiac patches are viable alternatives for the regeneration of myocardium after infarction through the effective integration of cardiac cells with the biomaterial.
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.
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".