Development of a Nanoparticle System for Controlled Release in Bioprinted Respiratory Scaffolds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of nanoparticle systems for the controlled release of growth factors is a promising approach to mimicking of the biochemical environment of native tissues in tissue engineering. However, sustaining growth factor release inside an appropriate therapeutic window is a challenge, particularly in bioprinted scaffolds. In this study, a chitosan-coated alginate-based nanoparticle system loaded with hepatocyte growth factor was developed and then incorporated into bioprinted scaffolds. The release kinetics were investigated with a focus on identifying the impact of the chitosan coating and culture conditions. Our results demonstrated that the chitosan coating decreased the release rate and lessened the initial burst release, while culturing in dynamic conditions had no significant impact compared to static conditions. The nanoparticles were then incorporated into bioinks at various concentrations, and scaffolds with a three-dimensional (3D) structure were bioprinted from the bioinks containing human pulmonary fibroblasts and bronchial epithelial cells to investigate the potential use of a controlled release system in respiratory tissue engineering. It was found that the bioink loaded with a concentration of 4 µg/mL of nanoparticles had better printability compared to other concentrations, while the mechanical stability of the scaffolds was maintained over a 14-day culture period. The examination of the incorporated cells demonstrated a high degree of viability and proliferation with visualization of the beginning of an epithelial barrier layer. Taken together, this study demonstrates that a chitosan-coated alginate-based nanoparticle system allows the sustained release of growth factors in bioprinted respiratory tissue scaffolds.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 it