Investigating How All‐Trans Retinoic Acid Polycaprolactone (atRA‐PCL) Microparticles Alter the Material Properties of 3D Printed Fibrin Constructs
Bibliographic record
Abstract
The 3D printing of human tissue constructs requires carefully designed bioinks to support the growth and function of cells. Here it is shown that an additional parameter is how drug-releasing microparticles affect the material properties of the scaffold. A microfluidic platform is used to create all-trans retinoic acid (atRA) polycaprolactone (PCL) microparticles with a high encapsulation efficiency (85.9 ± 5.0%), and incorporate them into fibrin constructs to investigate their effect on the material properties. An encapsulation that is around 25-35% higher than the current state of the art batch methods is achieved. It is also found that the drug loading concentration affects the microparticle size, which can be controlled using the microfluidic platform. It is shown that the release of atRA is slower in fibrin constructs than in buffer, and that the presence of atRA in the microparticles modulates both the degradation and the rheological properties of the constructs. Finally, it is shown that the fibrin material exhibits a stronger solid-like state in the presence of atRA-PCL microparticles. These findings establish a basis for understanding the interplay between drug-releasing microparticles and scaffold materials, paving the way for bioinks that achieve tailored degradation and mechanical properties, together with sustained drug delivery for tissue engineering 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".